ABSTRACT
Abstract
This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. patent application Ser. No. 18/409,744, filed Jan. 10, 2024, titled âSYSTEMS AND METHODS FOR BIOMETRIC IDENTIFICATION USING PATTERNS AND BLOOD FLOW CHARACTERISTICS OF THE OUTER EYEâ, which is a continuation-in-part of U.S. patent application Ser. No. 18/183,932, filed Mar. 14, 2023, titled âSYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE BASED BLOOD PRESSURE COMPUTATION BASED ON IMAGES OF THE OUTER EYEâ, which claims the benefit of: U.S. Provisional Application 63/319,738, filed Mar. 14, 2022, titled âSYSTEMS AND METHODS FOR REMOTE AND AUTOMATED MEDICAL DIAGNOSIS,â which is herein incorporated by reference in its entirety, U.S. Design application Ser. No. 29/830,662, filed Mar. 14, 2022, titled âCONSUMER ELECTRONICS DEVICE,â which is herein incorporated by reference in its entirety, and U.S. Provisional Application 63/424,048, filed Nov. 9, 2022, titled âSYSTEMS AND METHODS FOR REMOTE AND AUTOMATED MEDICAL DIAGNOSIS,â which is herein incorporated by reference in its entirety.
BACKGROUND
Field of Art
This invention pertains to the field of medical devices, with a specific focus on a hand-held multi-functional medical diagnostic device that integrates various health monitoring sensors in a compact and user-friendly form.
Discussion of the State of the Art
Biometric identification technologies generally rely on the principle that each individual has distinguishing characteristic(s) unique to that particular individual. In many cases this involves some sort of identifiable pattern (e.g. fingerprints, iris patterns, etc.) associated with a physical or biological characteristic of the individual. A problem with these patterns is that they can be artificially generated in order to fool biometric identification systems. For example, through advances in 3D printing technology, these patterns can be reproduced in 3D with the precision necessary to fool biometric scanners (e.g. via 3D models, contact lenses, etc.).
Currently available medical diagnostic devices are large, bulky devices with poor portability and usability. For example, devices like MRI machines, CT scanners, and research grade ECG/EEG equipment lack portability outside hospitals and dedicated facilities. Significant expertise is also required to operate them and interpret their results, limiting accessibility for general healthcare use.
Some have tried to condense the form factor of medical diagnostic devices, but these devices are severely limited with regards to their accuracy, reliability, and functionality. Additionally, these portable devices focus on a single diagnostic function because it is challenging to maintain the accuracy and reliability of each sensor while ensuring the device remains portable and easy to use. For example, it is challenging to minimize interference between sensor components, leading to inaccurate or skewed readings. Additionally, managing power efficiently to extend battery life is very difficult, particularly in a device that incorporates several sensors and functions. Broadly, the computation, connectivity, and sensor components required strain typical battery capacities, severely limiting run time between charges. This not only inconveniences end-users but also leads to gaps in health measurement data. Additionally, the complexity of synchronizing data from multiple sensors, running analysis algorithms, and providing user-friendly interfaces has often exceeded the processing capabilities that can be integrated given size and power constraints. Insufficient processing resources can lead to latency and errors in displaying important diagnostic results to end-users when they need it.
As a result, individuals seeking a comprehensive health assessment are required to use large and expensive machines, which provide high accuracy, but at the expense of inconvenience and cost, or portable devices with poor accuracy and reliability, and the use of multiple devices, which can be cumbersome and costly.
Additionally, many such medical devices lack the ability to provide real-time, personalized insights and recommendations to patients and healthcare providers. This may be possible by using AI systems, however, the computational requirements of AI algorithms often exceed the processing power and storage capacity available on small, portable devices. This limitation has posed a significant challenge in the development of AI-enabled medical devices.
Traditionally, developers have attempted to address this issue by relying on either on-device processing or cloud computing. On-device processing involves running simple, lightweight algorithms directly on the medical device itself. While this approach provides fast response times and can operate independently of network connectivity, it severely limits the complexity and sophistication of the AI algorithms that can be employed. On the other hand, cloud computing offloads all AI processing to remote servers, which can handle more advanced algorithms but introduces issues related to latency, network dependence, and data privacy concerns.
These conventional approaches to integrating AI into medical devices have proven suboptimal due to their inherent trade-offs and limitations. On-device processing sacrifices AI performance for local computation, while cloud computing introduces delays and relies on constant network availability. Moreover, both approaches raise concerns regarding the security and privacy of sensitive medical data, as it must be either stored on the device or transmitted to remote servers.
Another problem addressed by the present invention relates to the field of targeted advertising and personalized interaction in public or semi-public spaces using recognition systems. Current technologies in this field include various methods of identifying and analyzing individuals as they move through such spaces to deliver personalized content, including advertisements and interactive experiences. These technologies typically utilize cameras and sensors combined with AI-driven software to detect and recognize individuals based on facial features, movements, and sometimes even biometric data.
Previous attempts to solve the problem of delivering personalized content effectively have included the use of facial recognition technologies, voice recognition systems, and motion sensors that track the movements of individuals. These systems collect data and analyze it to tailor advertisements or informational content displayed on digital signage or broadcasted through audio systems. However, these solutions have several limitations.
These existing technologies often struggle with accuracy in diverse environmental conditions. For instance, poor lighting or crowded spaces can significantly decrease the reliability of facial recognition systems. Additionally, these systems generally require a direct line of sight to the individual, limiting their effectiveness in dynamic environments where obstructions are common.
Additionally, the adaptability of current systems is often lacking. Many are not equipped to learn or evolve based on interaction outcomes or environmental changes. This results in a static system that does not improve over time or adjust to new types of data or changes in user behavior, thereby diminishing the potential for truly personalized interactions.
While there are existing methods and technologies aimed at identifying individuals and delivering personalized content in public spaces, these methods are often hindered by issues of accuracy, privacy concerns, and lack of adaptability.
Diagnosing medical conditions and determining appropriate treatments can be a complex and time-consuming process for providers. Doctors must consider a wide range of information, including patient-reported symptoms, physical examination findings, sensor data and test results, as well as the provider's own knowledge and experience. Based on this information, the doctor must narrow down the list of potential diagnoses and decide on next steps, which may include ordering additional tests, prescribing treatments and/or medications, or referring the patient to a specialist.
Failing to consider all relevant information or appropriately weigh different factors can lead to misdiagnosis or suboptimal care. Even experienced providers may occasionally overlook a potential diagnosis or order unnecessary tests. Such mistakes can negatively impact patient outcomes, increase healthcare costs, and potentially expose doctors to malpractice liability if the standard of care was not followed.
Some tools exist to help doctors with the diagnostic process, such as reference books, online symptom checkers, and clinical decision support software. However, these tools have significant limitations. Reference materials contain a huge volume of information that a human cannot memorize or quickly sort through. Symptom checkers used by patients are very general and do not consider the full scope of information available to the provider. Existing clinical decision support systems are often rule-based, do not learn or improve over time, and/or are limited in the types and quantity of data used to generate the rule.
Therefore, there is a need for improved systems and methods to assist providers with medical diagnosis in a way that considers all available patient information, compares it to an extensive knowledge base, provides data-driven recommendations, and becomes more accurate and capable over time. The development of such a system faces technical challenges in processing multi-modal data, representing medical knowledge in a structured way, analyzing information in real-time during a patient encounter, and enabling the system to learn from feedback and additional data. Overcoming these challenges could significantly enhance the efficiency and accuracy of diagnosis.
Current health and fitness tracking devices, whether wearable or non-wearable, typically collect and display various sensor readings to the user. However, these devices face significant technical limitations in their ability to meaningfully interpret the data for the user. The devices lack the necessary algorithms and computational power to analyze the complex, multi-factorial sensor data in real-time to accurately identify potential health concerns.
Existing devices are often restricted to comparing each individual sensor reading to a pre-defined, generic range. However, these ranges fail to account for the numerous personal factors that impact a user's health such as age, race, height, pre-existing conditions, diet, exercise habits, and medications. The devices lack the technical capability to integrate and analyze these multiple data streams to generate personalized, dynamic ranges tailored to each specific user.
Moreover, current tracking devices operate in isolation, only analyzing the data collected by their own sensors. They are not configured to send and receive data from other sources such as the user's medical records, past lab results, or symptom logs. This siloed approach prevents the development of a comprehensive picture of the user's health required for accurate identification of potential concerns. The devices lack the interoperability and security features needed to gather sensitive medical data from disparate sources.
Even if existing devices could collect and integrate the necessary data streams, they do not possess the machine learning capabilities to identify complex patterns and relationships indicative of health issues. Conventional rule-based algorithms are insufficient to handle the intricacies and variability of human health across diverse populations. The devices lack the adaptive AI technologies required to refine their analysis over time based on cumulative user data.
Assuming the technical challenges of data integration and analysis could be overcome, current devices would still face difficulties in communicating results to the user. Providing a binary âhealthyâ or âunhealthyâ determination based on sensor data risks crossing the line into impermissible diagnosis. The devices lack the technical means to present a non-diagnostic yet actionable assessment to the user, such as a graded warning system, to empower informed decision-making.
Therefore, there is a need for a technically sophisticated system to collect and synthesize sensor data, medical records, and user-inputted information in real-time. The system requires advanced machine learning algorithms to identify user-specific patterns and generate personalized health assessments. Critically, the system must possess the technical capacity to present meaningful feedback to the user in a format that encourages appropriate action without offering a diagnosis. Overcoming these challenges requires an integrated, adaptive platform beyond the scope of current health tracking devices.
In the digital landscape, there is an increasing interest in the creation and expansion of virtual environments, specifically within the concept known as the metaverse. The metaverse is anticipated to offer an expansive, interconnected digital space, where individuals can interact, perform tasks, work, and even receive healthcare services through avatars. However, the transition from real-world activities into their digital counterparts poses several challenges, particularly in the realm of personal tasks such as healthcare appointments, within the metaverse.
One of the primary issues is the ability to receive healthcare services in a virtual environment without sacrificing the privacy and security of sensitive personal information. Traditional telehealth services often require the disclosure of private information, which can be susceptible to theft or misuse. These services do not always capitalize on the potential for anonymity, an aspect that can alleviate the discomfort some individuals feel when seeking certain types of healthcare.
Additionally, the seamless integration of real-world tasks, such as healthcare appointments, into the virtual environment confronts obstacles. While participants in the metaverse may desire the convenience of completing these tasks without departing from the virtual space, solutions that encompass the complex interplay between virtual activities and physical consequences are limited. This challenge accentuates the need for innovative approaches to embed real-world functionalities within the metaverse, all while maintaining a user-friendly and secure interface.
Another problem is the lack of sufficient security measures within virtual spaces, particularly concerning healthcare services. The current telehealth models do not fully exploit technologies like blockchain, which can offer enhanced security through data encryption and secure tokens. This inadequacy opens avenues for potential data breaches and identity theft, underscoring the necessity for improved security mechanisms in the crossover between healthcare and virtual environments.
Additionally, existing telehealth approaches in the real world suffer from inefficiencies due to a lack of multisensor based devices operable to provide sensor data transmitted from the patient to a provider to facilitate healthcare consultations. The same problem arises in virtual environments such as the metaverse which currently lack the ability for a patient to provide real-time sensor data from a compatible multisensor device.
Attempts to address these concerns within the nascent framework of the metaverse have been scarce, primarily due to its embryonic state and the technological limitations of current virtual reality systems. Existing virtual environments, such as those accessible through devices like the Oculus Quest, offer only rudimentary capabilities, limiting interactions to basic social and gaming activities without the complexity or scale envisioned for the metaverse. As such, the foundational technologies and approaches to facilitate comprehensive healthcare services, secure data exchange, and the integration of real-world tasks in these emerging digital universes remain underdeveloped.
The lack of precedent and existing solutions further complicates efforts
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. patent application Ser. No. 18/409,744, filed Jan. 10, 2024, titled âSYSTEMS AND METHODS FOR BIOMETRIC IDENTIFICATION USING PATTERNS AND BLOOD FLOW CHARACTERISTICS OF THE OUTER EYEâ, which is a continuation-in-part of U.S. patent application Ser. No. 18/183,932, filed Mar. 14, 2023, titled âSYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE BASED BLOOD PRESSURE COMPUTATION BASED ON IMAGES OF THE OUTER EYEâ, which claims the benefit of: U.S. Provisional Application 63/319,738, filed Mar. 14, 2022, titled âSYSTEMS AND METHODS FOR REMOTE AND AUTOMATED MEDICAL DIAGNOSIS,â which is herein incorporated by reference in its entirety, U.S. Design application Ser. No. 29/830,662, filed Mar. 14, 2022, titled âCONSUMER ELECTRONICS DEVICE,â which is herein incorporated by reference in its entirety, and U.S. Provisional Application 63/424,048, filed Nov. 9, 2022, titled âSYSTEMS AND METHODS FOR REMOTE AND AUTOMATED MEDICAL DIAGNOSIS,â which is herein incorporated by reference in its entirety.
BACKGROUND
Field of Art
This invention pertains to the field of medical devices, with a specific focus on a hand-held multi-functional medical diagnostic device that integrates various health monitoring sensors in a compact and user-friendly form.
Discussion of the State of the Art
Biometric identification technologies generally rely on the principle that each individual has distinguishing characteristic(s) unique to that particular individual. In many cases this involves some sort of identifiable pattern (e.g. fingerprints, iris patterns, etc.) associated with a physical or biological characteristic of the individual. A problem with these patterns is that they can be artificially generated in order to fool biometric identification systems. For example, through advances in 3D printing technology, these patterns can be reproduced in 3D with the precision necessary to fool biometric scanners (e.g. via 3D models, contact lenses, etc.).
Currently available medical diagnostic devices are large, bulky devices with poor portability and usability. For example, devices like MRI machines, CT scanners, and research grade ECG/EEG equipment lack portability outside hospitals and dedicated facilities. Significant expertise is also required to operate them and interpret their results, limiting accessibility for general healthcare use.
Some have tried to condense the form factor of medical diagnostic devices, but these devices are severely limited with regards to their accuracy, reliability, and functionality. Additionally, these portable devices focus on a single diagnostic function because it is challenging to maintain the accuracy and reliability of each sensor while ensuring the device remains portable and easy to use. For example, it is challenging to minimize interference between sensor components, leading to inaccurate or skewed readings. Additionally, managing power efficiently to extend battery life is very difficult, particularly in a device that incorporates several sensors and functions. Broadly, the computation, connectivity, and sensor components required strain typical battery capacities, severely limiting run time between charges. This not only inconveniences end-users but also leads to gaps in health measurement data. Additionally, the complexity of synchronizing data from multiple sensors, running analysis algorithms, and providing user-friendly interfaces has often exceeded the processing capabilities that can be integrated given size and power constraints. Insufficient processing resources can lead to latency and errors in displaying important diagnostic results to end-users when they need it.
As a result, individuals seeking a comprehensive health assessment are required to use large and expensive machines, which provide high accuracy, but at the expense of inconvenience and cost, or portable devices with poor accuracy and reliability, and the use of multiple devices, which can be cumbersome and costly.
Additionally, many such medical devices lack the ability to provide real-time, personalized insights and recommendations to patients and healthcare providers. This may be possible by using AI systems, however, the computational requirements of AI algorithms often exceed the processing power and storage capacity available on small, portable devices. This limitation has posed a significant challenge in the development of AI-enabled medical devices.
Traditionally, developers have attempted to address this issue by relying on either on-device processing or cloud computing. On-device processing involves running simple, lightweight algorithms directly on the medical device itself. While this approach provides fast response times and can operate independently of network connectivity, it severely limits the complexity and sophistication of the AI algorithms that can be employed. On the other hand, cloud computing offloads all AI processing to remote servers, which can handle more advanced algorithms but introduces issues related to latency, network dependence, and data privacy concerns.
These conventional approaches to integrating AI into medical devices have proven suboptimal due to their inherent trade-offs and limitations. On-device processing sacrifices AI performance for local computation, while cloud computing introduces delays and relies on constant network availability. Moreover, both approaches raise concerns regarding the security and privacy of sensitive medical data, as it must be either stored on the device or transmitted to remote servers.
Another problem addressed by the present invention relates to the field of targeted advertising and personalized interaction in public or semi-public spaces using recognition systems. Current technologies in this field include various methods of identifying and analyzing individuals as they move through such spaces to deliver personalized content, including advertisements and interactive experiences. These technologies typically utilize cameras and sensors combined with AI-driven software to detect and recognize individuals based on facial features, movements, and sometimes even biometric data.
Previous attempts to solve the problem of delivering personalized content effectively have included the use of facial recognition technologies, voice recognition systems, and motion sensors that track the movements of individuals. These systems collect data and analyze it to tailor advertisements or informational content displayed on digital signage or broadcasted through audio systems. However, these solutions have several limitations.
These existing technologies often struggle with accuracy in diverse environmental conditions. For instance, poor lighting or crowded spaces can significantly decrease the reliability of facial recognition systems. Additionally, these systems generally require a direct line of sight to the individual, limiting their effectiveness in dynamic environments where obstructions are common.
Additionally, the adaptability of current systems is often lacking. Many are not equipped to learn or evolve based on interaction outcomes or environmental changes. This results in a static system that does not improve over time or adjust to new types of data or changes in user behavior, thereby diminishing the potential for truly personalized interactions.
While there are existing methods and technologies aimed at identifying individuals and delivering personalized content in public spaces, these methods are often hindered by issues of accuracy, privacy concerns, and lack of adaptability.
Diagnosing medical conditions and determining appropriate treatments can be a complex and time-consuming process for providers. Doctors must consider a wide range of information, including patient-reported symptoms, physical examination findings, sensor data and test results, as well as the provider's own knowledge and experience. Based on this information, the doctor must narrow down the list of potential diagnoses and decide on next steps, which may include ordering additional tests, prescribing treatments and/or medications, or referring the patient to a specialist.
Failing to consider all relevant information or appropriately weigh different factors can lead to misdiagnosis or suboptimal care. Even experienced providers may occasionally overlook a potential diagnosis or order unnecessary tests. Such mistakes can negatively impact patient outcomes, increase healthcare costs, and potentially expose doctors to malpractice liability if the standard of care was not followed.
Some tools exist to help doctors with the diagnostic process, such as reference books, online symptom checkers, and clinical decision support software. However, these tools have significant limitations. Reference materials contain a huge volume of information that a human cannot memorize or quickly sort through. Symptom checkers used by patients are very general and do not consider the full scope of information available to the provider. Existing clinical decision support systems are often rule-based, do not learn or improve over time, and/or are limited in the types and quantity of data used to generate the rule.
Therefore, there is a need for improved systems and methods to assist providers with medical diagnosis in a way that considers all available patient information, compares it to an extensive knowledge base, provides data-driven recommendations, and becomes more accurate and capable over time. The development of such a system faces technical challenges in processing multi-modal data, representing medical knowledge in a structured way, analyzing information in real-time during a patient encounter, and enabling the system to learn from feedback and additional data. Overcoming these challenges could significantly enhance the efficiency and accuracy of diagnosis.
Current health and fitness tracking devices, whether wearable or non-wearable, typically collect and display various sensor readings to the user. However, these devices face significant technical limitations in their ability to meaningfully interpret the data for the user. The devices lack the necessary algorithms and computational power to analyze the complex, multi-factorial sensor data in real-time to accurately identify potential health concerns.
Existing devices are often restricted to comparing each individual sensor reading to a pre-defined, generic range. However, these ranges fail to account for the numerous personal factors that impact a user's health such as age, race, height, pre-existing conditions, diet, exercise habits, and medications. The devices lack the technical capability to integrate and analyze these multiple data streams to generate personalized, dynamic ranges tailored to each specific user.
Moreover, current tracking devices operate in isolation, only analyzing the data collected by their own sensors. They are not configured to send and receive data from other sources such as the user's medical records, past lab results, or symptom logs. This siloed approach prevents the development of a comprehensive picture of the user's health required for accurate identification of potential concerns. The devices lack the interoperability and security features needed to gather sensitive medical data from disparate sources.
Even if existing devices could collect and integrate the necessary data streams, they do not possess the machine learning capabilities to identify complex patterns and relationships indicative of health issues. Conventional rule-based algorithms are insufficient to handle the intricacies and variability of human health across diverse populations. The devices lack the adaptive AI technologies required to refine their analysis over time based on cumulative user data.
Assuming the technical challenges of data integration and analysis could be overcome, current devices would still face difficulties in communicating results to the user. Providing a binary âhealthyâ or âunhealthyâ determination based on sensor data risks crossing the line into impermissible diagnosis. The devices lack the technical means to present a non-diagnostic yet actionable assessment to the user, such as a graded warning system, to empower informed decision-making.
Therefore, there is a need for a technically sophisticated system to collect and synthesize sensor data, medical records, and user-inputted information in real-time. The system requires advanced machine learning algorithms to identify user-specific patterns and generate personalized health assessments. Critically, the system must possess the technical capacity to present meaningful feedback to the user in a format that encourages appropriate action without offering a diagnosis. Overcoming these challenges requires an integrated, adaptive platform beyond the scope of current health tracking devices.
In the digital landscape, there is an increasing interest in the creation and expansion of virtual environments, specifically within the concept known as the metaverse. The metaverse is anticipated to offer an expansive, interconnected digital space, where individuals can interact, perform tasks, work, and even receive healthcare services through avatars. However, the transition from real-world activities into their digital counterparts poses several challenges, particularly in the realm of personal tasks such as healthcare appointments, within the metaverse.
One of the primary issues is the ability to receive healthcare services in a virtual environment without sacrificing the privacy and security of sensitive personal information. Traditional telehealth services often require the disclosure of private information, which can be susceptible to theft or misuse. These services do not always capitalize on the potential for anonymity, an aspect that can alleviate the discomfort some individuals feel when seeking certain types of healthcare.
Additionally, the seamless integration of real-world tasks, such as healthcare appointments, into the virtual environment confronts obstacles. While participants in the metaverse may desire the convenience of completing these tasks without departing from the virtual space, solutions that encompass the complex interplay between virtual activities and physical consequences are limited. This challenge accentuates the need for innovative approaches to embed real-world functionalities within the metaverse, all while maintaining a user-friendly and secure interface.
Another problem is the lack of sufficient security measures within virtual spaces, particularly concerning healthcare services. The current telehealth models do not fully exploit technologies like blockchain, which can offer enhanced security through data encryption and secure tokens. This inadequacy opens avenues for potential data breaches and identity theft, underscoring the necessity for improved security mechanisms in the crossover between healthcare and virtual environments.
Additionally, existing telehealth approaches in the real world suffer from inefficiencies due to a lack of multisensor based devices operable to provide sensor data transmitted from the patient to a provider to facilitate healthcare consultations. The same problem arises in virtual environments such as the metaverse which currently lack the ability for a patient to provide real-time sensor data from a compatible multisensor device.
Attempts to address these concerns within the nascent framework of the metaverse have been scarce, primarily due to its embryonic state and the technological limitations of current virtual reality systems. Existing virtual environments, such as those accessible through devices like the Oculus Quest, offer only rudimentary capabilities, limiting interactions to basic social and gaming activities without the complexity or scale envisioned for the metaverse. As such, the foundational technologies and approaches to facilitate comprehensive healthcare services, secure data exchange, and the integration of real-world tasks in these emerging digital universes remain underdeveloped.
The lack of precedent and existing solutions further complicates efforts to bridge the gap between the traditional execution of tasks and their virtual analogs. While virtual reality technologies have laid the groundwork for simulated environments, they fall short of creating a fully immersive, secure, and integrated metaverse experience that encompasses complex interactions, such as healthcare, in a seamless and privacy-preserving manner. These shortcomings highlight the gap in current digital capabilities and underscore the necessity for innovative solutions tailored to the unique emerging demands of life within the metaverse.
SUMMARY
The present invention relates, in part, to biometric identification using a combination of static biological or physiological characteristics and active or dynamic biological or physiological characteristics of an individual. In particular, an identity of an individual is determined using a combination of a pattern characteristic unique to an individual and a measure of a dynamically changing blood flow characteristic. For example, the microvasculature of the outer eye (e.g. in the scleral region) presents a unique pattern for each individual which can be detected as described herein and combined with blood velocity characteristics through at least a portion of the same microvasculature of the outer eye. This combination of measures allows for identification of an individual without being able to be faked by current technology. That is, while patterns alone are becoming increasingly reproducible by artificial means, the actual blood flow characteristics of a living individual cannot be faked.
One novel approach to biometric identification described herein includes obtaining a first image, from a first camera, of the vasculature of the outer eye of an individual, obtaining a series of second images at a higher magnification than the first image, from a second camera, of the vasculature of the outer eye of the individual, applying a first AI algorithm to analyze the images from the first camera to determine at least one pattern characteristic associated with the eye vasculature, applying a second AI algorithm to analyze the images from the second camera to determine at least one blood flow characteristic (e.g. velocity) within the eye vasculature, and applying a third AI algorithm to determine an identity of the individual based on the combination of the analysis of the at least one pattern characteristic and the at least one blood flow characteristic. In one aspect, the third AI algorithm is operable to compare the at least one pattern characteristic and the at least one blood flow characteristic with a database of previously established pattern characteristics and blood flow characteristics for a plurality of individuals in order to determine identity.
Currently, there are no known conventional approaches to biometric identification techniques or systems which rely on the combination of eye vasculature patterns and dynamic blood flow characteristics. The present approaches allow for contactless, real-time biometric identification from computer vision and AI processing of images of the outer eye which is not known to exist in the prior art.
The present invention is for a hand-held medical diagnostic device that integrates multiple health monitoring sensors in a compact and user-friendly design. This approach overcomes the limitations of both large, stationary medical equipment as well as less reliable, single-function portable devices.
The handheld medical diagnostic device incorporates novel techniques and components for balanced integration. Multiple physiological sensors are combined to measure a wide range of health parameters, while proprietary calibration methods and sensor shielding maintain accuracy of and isolate potential interference between components. Complex yet efficient analysis algorithms are implemented in application-specific integrated circuits tailored for low-power parallel processing. These specially designed circuits synchronize output from the sensors, run diagnostics tests, and translate raw data into easy-to-understand health insights for the user. Power management is optimized between custom battery components and power-efficient hardware to enable extended operation times from a single charge. Additionally, an intuitive user interface with the ability to display instructions and diagnostic data that has been processed, for example, on board or in a connected cloud server. The total aggregation of custom engineered sensors, hardware, software, and power components enables comprehensive and accurate diagnostic capabilities within a compact, reliable, and accessible device.
Broadly, in accordance with an embodiment of the invention, the inventive device is composed of multiple sensor modalities into a single compact housing with the housing shaped to comfortably position the sensors against a user during operation. The sensor functions include, but are not limited to, otoscopy, high magnification skin and outer eye imaging, infrared thermometry, pulse oximetry, auscultation, electrocardiogramd body composition analysis. The measured results display on an integrated screen to provide diagnostic data and/or analytics in a single device.
An aspect of the inventive design is minimizing interference between these sensors. This is achieved through careful circuit design and the use of shielding techniques, which help maintain the accuracy and reliability of readings.
To address power management challenges, the device is equipped with a high-capacity battery and an intelligent power distribution system. This system dynamically allocates power to different sensors based on their current usage, optimizing overall battery life and maintaining consistent sensor performance.
The inventive device also incorporates an energy-efficient microprocessor, capable of handling the demands of processing data from multiple sensors. This processor, alongside optimized software algorithms, allows for swift and accurate data processing, reducing latency and potential errors in displaying diagnostic results.
The device also features a straightforward, high-resolution display interface, designed for case of use. This interface simplifies navigation and understanding of health data, making the device accessible to a broad range of users, regardless of their technical expertise.
The present invention provides a novel architecture for integrating artificial intelligence (AI) capabilities into handheld medical devices. The system leverages a distributed computing approach, partitioning AI processing tasks across on-device, edge, and cloud resources. This innovative architecture enables medical devices to deliver real-time, personalized insights and recommendations while overcoming the limitations of traditional on-device or cloud-only solutions.
The inventive system and process disclosed herein offers several improvements over existing approaches. By incorporating an edge processing layer, the architecture reduces latency, optimizes bandwidth usage, enhances data privacy and security, and improves overall system resiliency and scalability. The edge nodes, situated close to the medical devices, can perform intermediate AI computations, such as running machine learning models for pattern detection on sensor data streams. This allows for faster response times and reduces the amount of raw data transmitted to the cloud, ensuring efficient use of network resources and minimizing privacy risks.
One of the novel aspects of the invention lies in the intelligent partitioning of AI tasks across the three processing layers. The system employs a dynamic task allocation algorithm that considers factors such as computational complexity, data privacy requirements, and network conditions to determine the optimal distribution of AI workloads. For instance, the algorithm may assign simple rule-based algorithms and signal preprocessing tasks to the on-device layer, while offloading more complex pattern recognition and anomaly detection tasks to the edge nodes. The cloud layer is reserved for computationally intensive tasks, such as training deep learning models on large, diverse biomedical datasets and performing long-term data analysis.
Another novel feature of the invention is the use of a secure, hybrid communication protocol that ensures end-to-end encryption of sensitive medical data. The protocol employs a combination of lightweight cryptographic algorithms and hardware-based security modules to protect data at rest and in transit. When transmitting data from the device to the edge nodes, the system uses short-range, low-power communication technologies like Bluetooth Low Energy (BLE) or Wi-Fi Direct, while data exchange between the edge nodes and the cloud relies on cellular networks or Wi-Fi with robust security measures, such as Transport Layer Security (TLS) and Virtual Private Networks (VPNs). This hybrid approach guarantees the confidentiality and integrity of patient data throughout the distributed AI processing pipeline.
By leveraging a distributed computing approach and intelligently partitioning AI tasks across on-device, edge, and cloud resources, the system delivers real-time, secure, and scalable AI performance while addressing the shortcomings of traditional solutions, the disclosed architecture is an improvement in the technical field of AI and medical data processing system. It improves patient outcomes by improving the speed and efficacy of delivery of personalized healthcare services. Specifically, multisensor device-enhanced telehealth allows current telehealth (just an audio or audio/video call) to reach its full potential and become the entry point for every patient journey, saving patients time & money (avoiding unnecessary in-office follow-ups), increasing efficiency for providers, and increasing profits for insurers.
The disclosed invention presents a novel system for delivering personalized advertisements and interactions in public spaces using an integrated multi-sensor and AI-driven approach. This system is designed to enhance accuracy, address privacy concerns, and improve adaptability compared to existing technologies.
At a high level, the inventive solution incorporates an advanced array of imaging and sensing technologies, including cameras capable of high-resolution imaging across varying light conditions and sensors that can detect and analyze a broader range of biometric markers, such as gait and voice, beyond traditional facial recognition, and/or scleral microvasculature pattern detection. The AI component of the system utilizes machine learning algorithms optimized for real-time data processing and capable of dynamic learning. This enables the system to adapt to new data inputs and environmental changes over time, enhancing the personalization of content delivery.
The use of diverse sensors and associated AI algorithms, as disclosed in various embodiments of the invention, allows for high accuracy in individual recognition even in challenging environments. This addresses one of the limitations of prior art, which often fails in crowded spaces or in poor lighting conditions. By broadening the types of biometric markers and environmental factors it can process, the system is less likely to misidentify individuals, thereby improving the relevance and effectiveness of targeted content.
Furthermore, the various embodiments improve privacy safeguards by implementing advanced data handling protocols that anonymize personal data at the point of collection. This system design mitigates privacy concerns significantly by ensuring that personal data is not stored or processed in a manner that could lead to unauthorized access or misuse, making it a substantial improvement over prior solutions that involve storing and processing potentially sensitive biometric data.
The inventive embodiments provide an improved method for delivering personalized content in public spaces that is more accurate, respects user privacy to a greater extent, and is more adaptable to varying environmental conditions and data types than existing solutions is an improvement in the technical field of data processing and data analytics.
The present invention is a system and method for assisting providers (e.g. clinicians, physicians, nurses, etc.) with standard of care using artificial intelligence (AI). This AI based approach is designed to augment provider diagnosis by integrating patient statements, sensor data, and/or health records to suggest potential diseases and/or treatment steps. The AI system has the capacity to analyze large volumes of data more efficiently than human providers. This may assist a provider in reaching a diagnosis more efficiently and help to ensure that a given standard of care is being met. The system takes as input at least one of patient-reported symptoms, provider notes separate from and/or beyond patient-reported symptoms, and sensor data from medical and/or health/wellness devices. This information is processed and analyzed by at least one AI model to generate a list of potential diagnoses along with the likelihood of each diagnosis, and could include recommended next steps for a provider to take based on the relevant standard of care.
The AI model is trained on a large dataset comprised of at least one of patient-reported symptoms across a plurality of patients, provider notes associated with a plurality of patients, sensor data from medical devices, medical records associated with a plurality of patients, scientific literature, and expert provider knowledge. As such, it encapsulates a broad range of medical information that would be infeasible for an individual provider to memorize or reference in real-time. The model also learns from feedback provided by providers, allowing it to improve its performance over time.
For each potential diagnosis, the system may provide recommendations on next steps, such as additional tests to order and/or treatments to prescribe, based on accepted standards of care. This serves to remind providers of best practices and reduce variability in care.
By considering a comprehensive set of patient information and medical knowledge, and providing data-driven diagnostic support, the invention helps providers arrive at accurate diagnoses, and/or narrow down a list of potential diagnoses, more efficiently and with greater certainty. This has the potential to improve patient outcomes by reducing diagnostic errors and delays in treatment. It can also lower healthcare costs by avoiding unnecessary tests and procedures caused by misdiagnosis or inefficiently narrowing down potential diagnoses.
The invention improves upon existing solutions in several ways. First, it uses state-of-the-art AI and natural language processing techniques to analyze both structured and unstructured data, providing more holistic decision support. Second, it is designed to integrate seamlessly into a provider's workflow and provide real-time assistance during patient encounters. Third, the continuous learning capability allows the system to stay up-to-date with the latest medical knowledge, adapt to each provider's individual practice patterns that fit within an acceptable standard of care, and become more capable and accurate as it is trained on ever-expanding patient data (in type and volume). Together, these enhancements make the invention a powerful tool for augmenting and enhancing providers' diagnostic capabilities, leading to an improvement in speed and quality of patient care while reducing the cost of care.
The present invention is a system and method for providing users with personalized health assessments and warnings based on real-time analysis of sensor data, medical records, and user-inputted information. The system employs advanced machine learning algorithms to identify user-specific patterns and generate easily understandable feedback indicating potential health concerns.
The invention addresses the technical limitations of current health tracking devices by integrating and analyzing data from multiple sources in real-time. The system is configured to collect sensor readings from various wearable and non-wearable devices, as well as retrieve relevant medical records, lab results, and user-provided symptom information. By synthesizing these disparate data streams, the invention creates a comprehensive picture of the user's health, enabling more accurate identification of potential issues.
The machine learning algorithms employed by the invention are specifically designed to handle the complexity and variability of human health data across diverse populations. Unlike conventional rule-based systems, the invention's adaptive algorithms continuously refine their analysis based on cumulative user data, improving accuracy over time. The algorithms are capable of identifying subtle, user-specific patterns that may be indicative of health concerns, even when individual sensor readings fall within generic population-based ranges.
A key feature of the invention is its ability to provide meaningful, actionable feedback to users without crossing the line into diagnosis. The system generates a graded warning system, such as a color-coded scale or numerical rating, to indicate the severity of potential health concerns. This non-diagnostic yet informative approach empowers users to make informed decisions about seeking medical attention, reducing the risk of both unnecessary doctor visits and dangerous delays in care. This is not to say that the invention is incapable of diagnostics, but that one key application comprises use as a warning tool.
The invention represents a significant improvement over prior art solutions by overcoming their technical limitations in data integration, analysis, and user communication. The system's ability to collect and synthesize data from multiple sources in real-time, coupled with its advanced machine learning capabilities, allows for the generation of highly personalized and accurate health assessments. The invention's graded warning system provides users with easily understandable and actionable information, promoting proactive health management without encroaching on the domain of licensed medical professionals.
In summary, the present invention addresses the technical shortcomings of existing health tracking devices by providing an integrated, adaptive platform for real-time data analysis and personalized health feedback. By empowering users to make informed decisions about their health based on comprehensive, user-specific data analysis, the invention represents a significant advancement in the field of personal health monitoring.
The invention pertains to a system and method for integrating real-world functionalities, such as healthcare services, into a virtual environment known as the metaverse. This is achieved by utilizing digital avatars and enhanced security measures such as those based on digital ledgers and/or blockchain technology. The invention addresses the issue of maintaining anonymity and security while accessing healthcare and other services within the metaverse, without the need for users to leave the virtual environment to complete tasks that require interaction with the real world.
The invention involves creating avatars that users can customize to either closely resemble their real-world appearance or significantly differ from it to maintain anonymity, particularly during sensitive health consultations. Healthcare services are provided in a virtual setting that the user can access from their virtual residence or at a designated virtual healthcare facility. This virtual healthcare access eliminates the discomfort associated with traditional telehealth encounters, where the exchange of sensitive information or physical and/or psychological embarrassment presents privacy concerns.
The invention incorporates security measures, such as digital ledgers and/or blockchain technology, secure tokens, and the like, to securely manage and verify user identity without exposing sensitive personal information. This is accomplished by using secure tokens and possibly integrating IP history and/or behavioral analysis and/or previously stored health data to authenticate the user's identity further. These measures aim to enhance security and privacy in telehealth services.
The invention allows for seamless integration of real-world tasks within the metaverse. For example, users can complete healthcare visits and optionally have physical treatment items delivered to their real-world location. This feature leverages the blend of real-world and virtual-world functionalities, enhancing user convenience and time efficiency by allowing them to stay within the immersive experience of the metaverse.
The invention addresses the need for privacy, security, and functionality within the space of the metaverse. Existing virtual reality technologies and telehealth systems do not offer the level of integration, security, and user immersion provided by this invention. By facilitating a secure, convenient, and integrated approach to accessing healthcare and completing daily tasks in a virtual environment, this invention enables a more realistic and seamless transition between real-world activities and their digital counterparts in the metaverse.
The present invention relates to a system and method for integrating real-world functionalities into a virtual environment, such as the metaverse, by utilizing digital avatars and enhanced security measures based on digital ledger and/or blockchain technology. The system allows users to access various services, including healthcare services, within the metaverse without the need to leave the virtual environment to complete tasks that require interaction with the real world.
The system comprises a virtual environment, such as the metaverse, where users are represented by digital avatars. These avatars serve as a means for users to interact with the virtual environment and access various services, including healthcare services. The system also includes a digital ledger and/or blockchain-based security framework that ensures the anonymity and security of user data and transactions within the virtual environment.
When a user wishes to access a real-world service, such as healthcare, within the metaverse, they can do so through their digital avatar. The avatar may be one they created for the metaverse environment, or an altered version specifically for their healthcare interaction. The avatar interacts with a virtual representation of the service provider, such as a virtual clinic or hospital. The user can then request specific services, such as medical consultations, diagnostic tests, or treatment plans, through their avatar.
The system securely transmits the user's request and any relevant data, such as medical history or symptoms, to the real-world service provider using digital ledger and/or blockchain technology. This ensures that the user's data remains confidential and tamper-proof throughout the process. The service provider can then review the user's request and provide the necessary services or feedback within the virtual environment.
The digital ledger and/or blockchain-based security framework also facilitates secure payment transactions between the user and the service provider. The user can authorize payments using their digital avatar, and the transactions are recorded on the digital ledger and/or blockchain, ensuring transparency and immutability.
Throughout the process, the user's anonymity is maintained, as their real-world physical identity is not revealed to the service provider (however, all information regarding their identity and eligibility to receive appropriate treatment is validated during the interaction while the maximum amount of anonymity legally possible is maintained) unless explicitly authorized by the user. This allows users to access real-world services within the metaverse without compromising their privacy or security and while avoiding unnecessary embarrassment.
The invention also enables the integration of other real-world functionalities into the metaverse, such as education, commerce, and social services. By utilizing digital avatars and digital ledger and/or blockchain-based security measures, the system creates a seamless and secure interface between the virtual environment and the real world, enhancing the functionality and utility of the metaverse.
In summary, the present invention provides a system and method for integrating real-world services, such as healthcare, into a virtual environment, while maintaining user anonymity and data security through the use of digital avatars and digital ledger and/or blockchain technology. This innovation enhances the capabilities of the metaverse and enables users to access a wide range of services without leaving the virtual environment.
BRIEF DESCRIPTION OF THE DRAWING FIGURES
The accompanying drawings illustrate several embodiments and, together with the description, serve to explain the principles of the invention according to the embodiments. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary and are not to be considered as limiting of the scope of the invention or the claims herein in any way.
FIG. 1 illustrates an environment for remote and/or automated medical diagnosis in accordance with an exemplary embodiment of the invention.
FIG. 2 illustrates an example medical/consumer electronics device in accordance with an exemplary embodiment of the invention.
FIG. 3 illustrates an example telemedicine platform in accordance with an exemplary embodiment of the invention.
FIG. 4 illustrates a flowchart for remote and/or automated medical diagnosis in accordance with an exemplary embodiment of the invention.
FIG. 5 illustrates components of an exemplary computing device that supports an embodiment of the inventive disclosure.
FIG. 6 illustrates one embodiment of a standalone computing system that supports an embodiment of the inventive disclosure.
FIG. 7 illustrates an exemplary distributed computing network that supports an exemplary embodiment of the inventive disclosure.
FIG. 8 illustrates an exemplary overview of a computer system that supports an exemplary embodiment of the inventive disclosure.
FIG. 9 illustrates a flowchart for remote and/or automated medical diagnosis and provision of care in accordance with an exemplary embodiment of the invention.
FIG. 10 illustrates an exemplary overview of a process for computing blood pressure based on images of the eye according to one exemplary embodiment of the inventive disclosure.
FIG. 11 A illustrates an exemplary medical electronics device in accordance with an exemplary embodiment of the invention.
FIG. 11 B illustrates an exemplary AI processing module in accordance with an exemplary embodiment of the invention.
FIG. 12 illustrates a method of gathering and analyzing biometric information in accordance with an exemplary embodiment of the invention.
FIG. 13 illustrates exemplary systems and methods for artificial intelligence standard of care support in accordance with an exemplary embodiment of the invention.
FIG. 14 illustrates an exemplary AI support system in accordance with an exemplary embodiment of the invention.
FIG. 15 illustrates an exemplary process for providing AI support for differential diagnosis and/or standard of care in accordance with an exemplary embodiment of the invention.
FIG. 16 illustrates exemplary systems and methods for artificial intelligence based health warning in accordance with an exemplary embodiment of the invention.
FIG. 17 illustrates an exemplary AI health warning system in accordance with an exemplary embodiment of the invention.
FIG. 18 illustrates an exemplary process for providing AI based warnings of potential health concerns.
FIG. 19 illustrates exemplary systems and methods for artificial intelligence based health warning in accordance with an exemplary embodiment of the invention.
FIG. 20 illustrates an exemplary AI health warning system in accordance with an exemplary embodiment of the invention.
FIG. 21 illustrates an exemplary process for providing AI based warnings of potential health concerns.
FIG. 22 illustrates an exemplary system for the AI enabled multisensor connected telehealth system.
FIG. 23 illustrates an exemplary process for implementing the AI enabled multisensor connected telehealth system.
FIG. 24 illustrates an exemplary system for context aware data system using biometric and identifying data.
FIG. 25 illustrates an exemplary process for implementing a context aware data system using biometric and identifying data.
FIG. 26 illustrates an exemplary multi sensor handheld medical diagnostic device.
FIG. 27 illustrates an exemplary multi sensor handheld medical diagnostic device.
FIG. 28 illustrates an exemplary system for the AI enabled multisensor connected telehealth system
DETAILED DESCRIPTION OF EMBODIMENTS
The present invention is for a hand-held medical diagnostic device that integrates multiple health monitoring sensors in a compact and user-friendly design. The invention is described by reference to various elements herein. It should be noted, however, that although the various elements of the inventive apparatus are described separately below, the elements need not necessarily be separate. The various embodiments may be interconnected and may be cut out of a singular block or mold. The variety of different ways of forming an inventive apparatus, in accordance with the disclosure herein, may be varied without departing from the scope of the invention.
Generally, one or more different embodiments may be described in the present application. Further, for one or more of the embodiments described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the embodiments contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous embodiments, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the embodiments, and it should be appreciated that other arrangements may be utilized and that structural changes may be made without departing from the scope of the embodiments. Particular features of one or more of the embodiments described herein may be described with reference to one or more particular embodiments or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular embodiments or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the embodiments nor a listing of features of one or more of the embodiments that must be present in all arrangements.
Headings of sections provided in this patent application and the title of this patent application are for convenience only and are not to be taken as limiting the disclosure in any way.
Devices and parts that are connected to each other need not be in continuous connection with each other, unless expressly specified otherwise. In addition, devices and parts that are connected with each other may be connected directly or indirectly through one or more connection means or intermediaries.
A description of an aspect with several components in connection with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible embodiments and in order to more fully illustrate one or more embodiments. Similarly, although process steps, method steps, or the like may be described in a sequential order, such processes and methods may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the embodiments, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, or method is carried out or executed. Some steps may be omitted in some embodiments or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.
When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article.
The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other embodiments need not include the device itself.
Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular embodiments may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. A
CLAIMS
Claims ( 17 )
The invention claimed is:
1. A multisensor-connected, device/edge/cloud AI-enabled, patient-centered health record-storing, telehealth-enhancing system for assisting a provider with differential diagnosis and standard of care and assisting a user/patient with health concern early warning and/or diagnosis, the system comprising:
a multi-sensor medical device comprising:
a plurality of sensors comprising at least seven of: a high-magnification camera module, a motorized camera module, a stethoscope module, an infrared thermopile sensor module, an electrocardiogram (EKG) sensor module, a pulse oximeter module, a body composition monitor module, a glucometer module, a hematology analyzer module, and/or a gyroscope sensor;
a housing enclosing at least the plurality of sensors, a system on chip (SoC) processor, the wireless transceiver, a battery, and the display;
the SoC configured to:
receive patient information from the plurality of sensors;
preprocess the patient information by applying an adaptive feature extraction algorithm;
securely transmit the preprocessed patient information to a cloud-based platform using an end-to-end encryption scheme;
the cloud-based platform comprising:
a secure data storage system for storing the preprocessed patient information;
a computing cluster configured to:
analyze the preprocessed patient information using an ensemble of deep learning models, each model being trained on a specific type of telehealth data and fine-tuned using transfer learning and domain adaptation techniques, the ensemble of deep learning models comprising a modular, extensible architecture and a multi-modal attention fusion module;
generate a ranked list of potential diagnoses and a likelihood score for each potential diagnosis using the ensemble of deep learning models;
an interactive telehealth module configured to communicate with a provider device and the multisensor medical device and transmit information among them and the cloud-based platform, the interactive telehealth module configured to:
send the ranked list of potential diagnoses, the likelihood scores, and a visualization of the factors contributing to each diagnosis to the provider device, the visualization comprising an attention mechanism that highlights a predefined set of salient features for each diagnosis;
receive feedback from the provider device indicating an appropriateness of the potential diagnoses and any additional insights;
send appropriate next steps and resulting insights and documentation to cloud storage, cloud-based AI modules, edge devices of approved users, and the multi-sensor medical device; and
an interactive telehealth portal configured to allow a provider device to communicate with the cloud-based platform, an edge compute node, and the multi-sensor medical device and also displaying data of various forms on a dashboard, dashboard data comprising a live voice and/or video call, a patient chart, raw live sensor data, pre-recorded sensor data, subjective statements of the patient, prior health records, and the above-referenced telehealth module visualization enabling feedback and continuous training.
2. The system of claim 1 , wherein the computing cluster:
continuously updates the ensemble of deep learning models using an incremental learning algorithm that adapts the model parameters and architecture based on new data without forgetting previously learned knowledge, the incremental learning algorithm comprising elastic weight consolidation, synaptic intelligence, and memory-augmented neural networks;
continuously fine-tunes the ensemble of deep learning models based on the received patient information and provider feedback, the fine tuning comprising a reward function that considers the accuracy of the diagnoses, the efficiency of the diagnostic process, and the long-term patient outcomes;
generates alerts for the provider when deviations from normal physiological patterns are detected, the alerts being accompanied by explainable AI visualizations that highlight the specific signal segments and features contributing to the anomaly; and
generates alerts for the patient when deviations from normal physiological patterns are detected, the alerts being accompanied by explainable AI visualizations that provide an early warning/severity score and/or diagnosis for the patient/user.
3. The system according to claim 1 , wherein the adaptive feature extraction algorithm is configured to:
automatically select and extract a predefined set of features from the raw sensor data based on their relevance to the diagnostic task and the quality of the signal;
adapt the feature extraction process to the specific characteristics and limitations of each sensor modality; and
continuously update the feature extraction parameters based on user feedback and provider annotations to improve performance over time.
4. The system according to claim 1 , wherein the end-to-end encryption is configured to:
encrypt the patient information at the point of collection using hardware-based encryption modules embedded within the multi-sensor medical device;
securely transmit the encrypted patient information to the cloud-based platform using secure communication protocols, such as transport layer security (TLS) or datagram transport layer security (DTLS); and
decrypt the patient information only within the secure processing environment of the cloud-based platform, ensuring that the data remains protected throughout the entire pipeline.
5. The system according to claim 1 , wherein the ensemble of deep learning models is configured to:
process the preprocessed patient information using a combination of convolutional neural networks (CNNs) for image and video data, recurrent neural networks (RNNs) for time-series data, and transformer networks for unstructured text data;
extract a predefined set of high-level features and patterns from the preprocessed data that are predictive of different medical conditions and diagnoses;
integrate the outputs of the individual models using a multi-modal attention fusion module that assigns different weights to each modality based on its relevance to the diagnostic task; and
continuously adapt to new patient populations and telehealth settings using domain adaptation techniques, such as adversarial training and domain-invariant feature learning.
6. The system according to claim 1 , wherein the interactive telehealth module is configured to:
establish a real-time video conferencing session between the provider device and the patient device, the video conferencing session being enhanced by a video quality optimization algorithm that adapts to the available network bandwidth to ensure a smooth, uninterrupted video stream;
display the ranked list of potential diagnoses, likelihood scores, and visualizations of a plurality of actors contributing to each diagnosis in an interactive, customizable dashboard that allows providers to access the supporting evidence and adjust the display settings based on their preferences; and
provide a secure messaging system for exchanging information between the provider and the patient, the messaging system incorporating encryption and access control mechanisms to protect the confidentiality and integrity of the exchanged messages.
7. The system according to claim 1 , wherein the reinforcement learning module is configured to:
model the diagnostic process as a sequential decision-making problem, by selecting a predefined set of informative tests and observations at each step to arrive at a diagnosis;
learn an optimal diagnostic policy that maximizes the expected cumulative reward over time, where the reward function considers accuracy of the diagnoses, efficiency of diagnostic process, and long-term patient outcomes;
explore alternative diagnostic strategies using techniques such as Monte Carlo tree search and upper confidence bound algorithms, allowing the system to discover novel approaches that outperform existing guidelines; and
continuously adapt the diagnostic policy based on provider feedback and patient outcomes, using techniques such as policy gradients and Q-learning to update the model parameters in real-time.
8. The system according to claim 1 , wherein the anomaly detection module is configured to:
continuously monitor the patient's physiological signals and compare them to personalized baselines and population-level norms;
identify deviations and patterns that may indicate the onset or progression of a medical condition, using techniques such as unsupervised outlier detection, one-class classification, and time-series segmentation;
generate real-time alerts and notifications for the provider when anomalies are detected, along with explanations of the specific signals and features that contributed to the alert; and
automatically trigger additional diagnostic tests or interventions based on the detected anomalies, using a decision support algorithm that considers the expected benefits, risks, and costs of each action.
9. A method for providing telehealth recommendations to a medical service provider, the method comprising:
obtaining patient data using a hand-held multi-sensor medical device, wherein the hand-held multi-sensor medical device comprises at least seven of: a high-magnification camera module, a motorized camera module, a stethoscope module, an infrared thermopile sensor module, an electrocardiogram (EKG) sensor module, a pulse oximeter module, a body composition monitor module, a glucometer module, a hematology analyzer module, and/or a gyroscope sensor;
preprocessing the collected patient data using feature extraction techniques;
securely transmitting the preprocessed patient data to a remote computing platform using encryption techniques;
sending instructions to the remote computing platform to analyze the encrypted patient data using a hierarchical machine learning model to generate potential diagnoses and potential treatment recommendations;
obtaining the potential diagnoses and potential treatment recommendations based on the analysis of the encrypted patient data and sending the obtained potential diagnoses and potential treatment recommendations to a computing device associated with a provider;
receiving feedback from the computing device associated with the provider via a telehealth interface;
sending instructions to the remote computing platform for adapting the potential recommendations based on provider feedback by using a reinforcement learning module;
enabling secure and privacy-preserving collaborative training of machine learning models across the hand-held multi-sensor medical device, the remote computing platform and the computing device associated with the provider using a federated learning module; and
enabling privacy-preserving analysis and decision-making on the encrypted patient data by multiple stakeholders using a multi-party computation module across the hand-held multi-sensor medical device, the remote computing platform and the computing device associated with the provider.
10. The method of claim 9 , wherein preprocessing the collected patient data includes applying at least one of: time-domain analysis, frequency-domain analysis, time-frequency domain analysis, principal component analysis (PCA), independent component analysis (ICA), convolutional neural networks (CNNs), recurrent neural networks (RNNs), discrete wavelet transform (DWT), empirical mode decomposition (EMD), and deep metric learning.
11. The method of claim 9 , wherein securely transmitting the preprocessed patient data includes employing a hybrid encryption scheme that combines symmetric and asymmetric encryption techniques, including a lightweight symmetric encryption algorithm for real-time data transmission and a homomorphic encryption scheme for privacy-preserving computation on the encrypted data.
12. The method of claim 9 , wherein processing the encrypted patient data using the hierarchical machine learning model includes employing a convolutional neural network (CNN) for extracting spatial features, a recurrent neural network (RNN) for modeling temporal dependencies, and a graph convolutional network (GCN) for integrating multimodal data.
13. The method of claim 9 , wherein adapting the personalized recommendations based on provider feedback includes employing a deep Q-network (DQN) algorithm to learn an optimal policy for generating personalized recommendations based on provider feedback and patient outcomes.
14. The method of claim 9 , wherein detecting anomalies in the patient's data includes employing at least one of: one-class support vector machines (SVMs), deep autoencoder networks, and Bayesian nonparametric models.
15. The method of claim 9 , wherein enabling secure and privacy-preserving collaborative training of machine learning models includes employing secure aggregation and differential privacy techniques to protect the privacy of the patient data during collaborative model training.
16. The method of claim 9 , wherein enabling privacy-preserving analysis and decision-making on the patient's data includes employing secret sharing or garbled circuits techniques to enable privacy-preserving computation on the patient's data by multiple parties.
17. The method of claim 9 , further comprising:
detecting anomalies in the encrypted patient data using an anomaly detection module;
continuously monitoring the patient's physiological signals and compare them to personalized baselines and population-level norms;
identifying deviations and patterns that may indicate the onset or progression of a medical condition, using techniques such as unsupervised outlier detection, one-class classification, and time-series segmentation;
generating real-time alerts and notifications for the provider when anomalies are detected, along with explanations of the specific signals and features that contributed to the alert; and
automatically triggering additional diagnostic tests or interventions based on the detected anomalies, using a decision support algorithm that considers the expected benefits, risks, and costs of each action.
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