AI-Driven Programs for Non-specific Chronic Neck Pain

Non-specific chronic neck pain represents a major global health burden, affecting 30% to 50% of the general population. It is particularly prevalent among office workers, computer users, and women, with a notable rise in young adults aged 20 to 24. While acute episodes of neck pain may initially resolve, underlying functional impairments often persist, causing over a third of patients to develop chronic symptoms lasting three months or longer. Typically localized in the lateral and posterior neck regions without radicular signs, this condition is frequently driven by poor posture and improper ergonomics. The resulting abnormal stress on the cervical spine and musculature severely limits daily activities, lowers overall quality of life, and places a heavy socioeconomic strain on patients and their communities. Traditional conservative management typically relies on a combination of pharmacotherapy and standard physical therapy modalities, including manual techniques, exercise programs, electrotherapy, and thermal agents. However, these conventional interventions demand frequent, in-person clinical visits, creating a significant financial and logistical barrier for many families, particularly under current economic challenges in Egypt. Consequently, there is an urgent need for cost-effective, highly accessible, and novel rehabilitation models that can streamline care and optimize clinical outcomes. Artificial intelligence (AI) and machine learning offer a promising solution to these challenges by providing automated, data-driven remote care. Through mobile apps and smart rehabilitation platforms, AI can analyze complex clinical datasets-including patient demographics, pain intensity, and radiographic alignment-to predict treatment timelines and automate routine clinical tasks. Crucially, AI solves the problem of standardized, non-individualized home exercise plans by adjusting to a patient's daily symptom presentation, mimics a therapist's tailored approach, and off