Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.

Artificial Intelligence and Predictive Analytics in Obstetric Anesthesia: Early Warning for Maternal Complications - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Curr Opin Anaesthesiol . Author manuscript; available in PMC: 2026 Jun 4. Published in final edited form as: Curr Opin Anaesthesiol. 2026 Feb 11;39(3):231–239. doi: 10.1097/ACO.0000000000001613 Artificial Intelligence and Predictive Analytics in Obstetric Anesthesia: Early Warning for Maternal Complications Vesela P Kovacheva a , Michael L Burns b Author information Article notes Copyright and License information a Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA b Department of Anesthesiology, University of Michigan, Ann Arbor, MI. ✉ Corresponding Author: Vesela P. Kovacheva M.D. Ph.D., 75 Francis St, L1, Boston, MA 02115, Fax: 617-730-2825, vkovacheva@bwh.harvard.edu Issue date 2026 Jun 1. PMC Copyright notice PMCID: PMC13231824  NIHMSID: NIHMS2180899  PMID: 41684266 The publisher's version of this article is available at Curr Opin Anaesthesiol Abstract Purpose of review: Maternal morbidity and mortality remain largely preventable, yet current risk-assessment tools identify only a fraction of women who will experience severe complications. This review synthesizes recent advances in artificial intelligence (AI) and machine learning (ML) for early prediction, decision support, and procedural guidance in obstetric anesthesia, with a focus on postpartum hemorrhage, hypertensive disease, sepsis, hemodynamic instability, neuraxial procedures, and peripartum pain. Recent findings: Over the past two to three years, EHR-integrated and imaging-based ML models have outperformed traditional risk scores for postpartum hemorrhage, placenta accreta spectrum, and preeclampsia, and are beginning to incorporate multi-omics and genetic data. Obstetric-specific early warning systems and parsimonious ML models for maternal sepsis and epidural-related fever show promise but remain limited by sensitivity and external validation. Waveform analytics, noninvasive hemodynamic indices, and AI-assisted ultrasound can anticipate hypotension and enhance neuraxial and regional block placement. ML frameworks for post-cesarean and chronic postpartum pain, together with virtual reality interventions, support more individualized analgesia. Summary: AI-enabled tools are poised to augment, rather than replace, clinician judgment in obstetric anesthesia. Real-world impact will depend on rigorous external validation, equitable implementation, interpretable model design, seamless EHR integration, and close collaboration between clinicians, data scientists, and vendors. Keywords: machine learning, predictive analytics, pregnancy, maternal outcomes, obstetric anesthesia Introduction Most maternal morbidity and mortality remain preventable, yet current practices have not significantly advanced maternal safety outcomes. Artificial intelligence (AI), particularly machine learning (ML), offers promising tools to bridge this gap by enabling early identification and timely intervention in maternal complications. In this review, we critically examine recent literature, emphasizing groundbreaking research and future directions for predictive analytics in obstetric anesthesia. An extensive literature search was conducted across PubMed, CINAHL Plus, Google Scholar, Scopus, Embase, and IEEE Xplore, while excluding studies exhibiting methodological limitations or lacking clear applications. Synthesizing and appraising recent advances in AI-driven predictive models, this review provides anesthesiologists and clinicians with valuable insights into state-of-the-art technologies poised to enhance maternal safety through proactive complication management. Text of review 1. Postpartum hemorrhage and placenta accreta spectrum Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal morbidity and a major focus for AI-enabled early warning in obstetric anesthesia. Recent ML models leveraging routinely collected electronic health record (EHR) data outperform traditional risk scores such as the California Maternal Quality Care Collaborative guidelines and enable earlier activation of hemorrhage protocols, with direct implications for neuraxial timing, blood product mobilization, and multidisciplinary response [ 1 - 3 ]. These models are built from delivery admission data and developed in both high- and low-resource settings, including Kenyan maternity units [ 3 ]. Interpretable approaches, such as the multicenter Obstetric Clinical Outcomes Assessment Program-based model, together with recent systematic reviews, now position ML-enhanced risk tools within standardized hemorrhage bundles and regulatory expectations, including Joint Commission requirements for evidence-based PPH risk assessment [ 1 , 4 - 6 ]. Despite these advances, robust external validation remains a major barrier to implementation. Models derived from large multicenter cohorts, including the Consortium on Safe Labor (CSL) dataset [ 7 , 8 ], demonstrate attenuated performance when transported to single institutions, alternative health systems, or settings that use quantified rather than estimated blood loss [ 9 , 10 ]. These discrepancies highlight the influence of local practice patterns, outcome definitions, and data quality on model generalizability. Several groups have prioritized inherently interpretable models and post hoc explanation methods such as SHAP-based feature attributions to enhance clinician trust and facilitate bedside adoption [ 1 , 11 ]. Despite these efforts, systematic reviews and meta-analyses highlight concerns: while pooled discrimination is generally good (AUC ~0.8–0.9), there is marked heterogeneity in populations, outcome definitions, and modeling strategies; external validation remains uncommon [ 4 , 5 ]. Radiomics and other advanced imaging-derived features are increasingly explored both for prediction of PPH and for placenta accreta spectrum (PAS). For PAS, radiomics-based analyses of ultrasound and T2-weighted MRI are used to identify patients at high risk of surgical complexity or massive hemorrhage, supporting multidisciplinary planning and resource allocation [ 12 , 13 ]. Complementary work has compared radiomics, deep-learning, and fusion models to predict PPH using MRI, suggesting that multimodal fusion of imaging and clinical features may offer incremental gains in discrimination [ 14 ]. Broader reviews of ML and radiomics for PAS and meta-analyses of MRI-based radiomics, report high diagnostic accuracy but also substantial methodological heterogeneity and reliance on small, single-center datasets, underscoring the need for larger, rigorously designed cohorts before routine clinical adoption [ 15 , 16 ]. Integrative radiomics (clinical nomograms and AI-assisted image interpretation platforms) are emerging, which match or exceed expert radiologist performance in PAS detection. This may standardize assessment across institutions, although they remain largely investigational [ 15 , 16 ]. Finally, metabolomic and lipidomic profiling combined with clinical data has been applied to PAS, providing additional modalities to risk stratification [ 17 ]. The timing and format of risk estimation are also evolving. While many ML tools generate risk scores at delivery admission, second-trimester models have demonstrated that meaningful PPH risk stratification is feasible earlier in pregnancy, with implications for antenatal counseling and delivery planning [ 18 ]. Several groups have translated complex models into pragmatic tools such as web-based calculators that can be integrated into workflow, including applications focusing on post-delivery hemoglobin prediction [ 2 , 19 ]. Subgroup-specific models, such as those designed for twin pregnancies, further illustrate the potential for precision risk assessment, although most have been validated only in relatively small cohorts [ 20 ]. Taken together, current work on AI-driven prediction of PPH and PAS underscores both technical feasibility and translational challenges of early-warning systems in obstetric anesthesia. The next phase will require larger, diverse prospective cohorts, harmonized outcome definitions, and rigorous evaluation to change transfusion practices, anesthetic decision-making, and maternal outcomes in real-world care. 2. Maternal cardiovascular morbidity: preeclampsia and peripartum cardiomyopathy ML algorithms now provide powerful tools to identify patients at risk for hypertensive disorders and cardiovascular complications during pregnancy. Traditional screening strategies rely on maternal medical history and detect only a minority of women who will develop preeclampsia, often in the range of ~40% of preterm cases with ~10% false-positive rate, leaving substantial residual risk [ 21 , 22 ]. In contrast, recent ML-based risk assessments leverage high-dimensional clinical data and dynamic longitudinal information, offering improved discrimination and calibration. Systematic and narrative reviews highlight that most preeclampsia models remain single-center, heterogeneously define outcomes, and rarely undergo rigorous external validation, limiting widespread implementation [ 23 , 24 ]. For anesthesiologists, these advances have direct implications for planning the timing and setting of delivery, anesthetic technique, invasive monitoring, and postpartum surveillance. A notable recent development is the PIERS-ML model [ 25 ], which integrates routinely collected clinical observations and laboratory data to estimate the short-term risk of severe adverse maternal outcomes among women with established or suspected preeclampsia. Similar models predict preeclampsia risk throughout pregnancy, model non-linear trajectories of blood pressure and laboratory values, and update risk at multiple gestational time points, as early as before 16 weeks when using routine laboratory data [ 26 - 29 ]. Integration of high-dimensional biomarker data further improves early identification [ 30 ]. Collectively, these approaches complement first- and second-trimester screening by offering continuous, personalized risk estimates across gestation. Genetic data integration enhances predictive capabilities of recent ML models. Combining polygenic risk scores with clinical features suggests that genetic susceptibility tracks with higher blood pressures across pregnancy, but the incremental gain in predictive performance beyond well-specified clinical and EHR-based models is modest, underscoring the need for careful evaluation of when genetic data meaningfully alters risk classification [ 31 , 32 ]. Incorporating genetic susceptibility alongside conventional clinical risk factors not only improves predictive accuracy but also highlights a subgroup of women at persistently high cardiovascular risk, with potential implications for postpartum and lifetime cardiovascular surveillance. Beyond preeclampsia-specific tools, recent work has introduced ML-based scores such as PARCCS, which use national inpatient data to estimate the risk of acute peripartum cardiovascular and renal complications, including peripartum cardiomyopathy (PPCM) during delivery admissions. These models again highlight potential and limitations (AUC ≈ 0.68) of real-world triage and monitoring decisions [ 33 ]. Emerging risk-prediction models built on national inpatient datasets estimate the likelihood of acute peripartum cardiovascular and renal complications, including preeclampsia/eclampsia, PPCM, venous thromboembolism, arrhythmias, and pulmonary edema, using parsimonious sets of routinely available variables. AI-enabled electrocardiography and digital stethoscopes have shown excellent performance for detecting peripartum cardiomyopathy and left ventricular systolic dysfunction in pregnant and postpartum women, with deep-learning ECG models achieving AUCs ≈0.9 and maintaining high performance in racially diverse populations, offering a potential screening layer before anesthetic decision-making [ 34 , 35 ]. These tools may enable earlier recognition of PPCM and timelier optimization of anesthetic management, fluid strategies, and postpartum monitoring for women with latent or overt cardiac disease. Across these models, interpretability has become a design requirement: trajectory-based predictors and PIERS-ML explicitly identify the variables and time-varying patterns that drive risk estimates [ 25 , 29 ], essential for clinician confidence, multidisciplinary collaboration, and real-world adoption in obstetric anesthesia. 3. Maternal sepsis and intrapartum fever Sepsis is a life-threatening complication in pregnancy and the postpartum period, and its early recognition directly informs decisions about neuraxial placement, antibiotic timing, and escalation to intensive care. In the general hospital population, sepsis affects ~4% of admissions [ 36 ]. AI-enabled sepsis prediction has evolved from rule-based alerts to complex ML tools. In 2024, the US FDA granted De Novo authorization to Sepsis ImmunoScore , the first AI-based sepsis diagnostic that integrates demographics, vital signs, laboratory values, and biomarkers [ 37 ]. At the same time, experience with the Epic Sepsis Model illustrates the risks of poor calibration and alert fatigue: independent validation found modest discrimination (AUC 0.63) [ 38 ], and during COVID-19 the proportion of inpatients triggering alerts increased sharply despite lower census [ 39 ]. These observations highlight the need for obstetric-specific validation, continuous performance monitoring, and local governance before embedding sepsis AI tools into labor and delivery workflows. In obstetrics, maternal sepsis complicates ~0.04% of deliveries but contributes to nearly a quarter of maternal deaths in the United States [ 40 ]. Pregnancy-specific ML models built from perinatal EHRs demonstrate that sepsis risk can be predicted using a relatively small set of vital signs and clinical history variables, reinforcing the potential value of local pregnancy-tailored models [ 41 ]. Data from the WHO Global Maternal Sepsis Study show that obstetric and sepsis-specific early warning systems achieve high sensitivity, while pregnancy-adapted qSOFA and SOFA variants are more specific. ML-based combinations of routine markers, however, demonstrate low sensitivity, supporting the use of early warning systems (EWS) as adjuncts rather than stand-alone diagnostics [ 42 ]. National maternity EWS systematic reviews emphasize that existing scores are methodologically weak and rarely validated, underscoring the need for statistically robust, externally validated, and implementation-ready obstetric tools [ 43 , 44 ]. In parallel, digital-health innovations such as wearable biosensors, mobile platforms, and connected diagnostics may enable earlier infection recognition, particularly in resource-limited settings [ 45 ]. Fever at term labor, particularly epidural-related maternal fever, adds complexity, as noninfectious intrapartum fever can trigger sepsis pathways, broaden antibiotic use, and delay or complicate neuraxial management. Emerging ML models specifically target these scenarios. A recent single-center study developed epidural-related maternal fever prediction models using several ML algorithms on women receiving patient-controlled epidural analgesia, identifying a compact set of predictors and demonstrating reasonable discrimination [ 46 ]. Complementary work from a large multicenter retrospective cohort used pre-labor analgesia clinical indicators to build and validate intrapartum fever prediction models; the best-performing model achieved an AUC of 0.71, with higher sensitivity (0.753) than specificity (0.584) but using only six interpretable predictors: primiparity, estimated neonatal weight, cervical dilatation, anemia, number of vaginal examinations, and maternal height [ 47 ]. Across these studies, a consistent theme emerges: simpler, parsimonious models based on a small number of well-chosen clinical variables may offer the best balance between performance, interpretability, and ease of implementation. 4. Hemodynamic instability during vaginal and cesarean delivery Predicting and managing hemodynamic instability is critical for anesthetic safety during labor and cesarean delivery. Spinal anesthesia–induced hypotension is common and linked to nausea, vomiting, and fetal acidosis, so practice emphasizes proactive vasopressor infusions rather than reactive boluses. AI–enabled hemodynamic prediction is emerging as a complementary strategy. The Hypotension Prediction Index (HPI) uses machine-learning analysis of arterial pressure waveforms to forecast hypotension minutes before it occurs [ 48 ]. Although most validation studies are in non-obstetric surgery, HPI-guided management reduces the duration and depth of intraoperative hypotension [ 49 ], and pilot work suggests waveform-based prediction is feasible during cesarean spinal anesthesia [ 49 ]. Such tools could allow earlier vasopressor titration and individualized blood pressure targets in high-risk patients. Simpler, low-cost predictors remain important, particularly where advanced monitors are unavailable. Shock Index–based models [ 50 ] and pulse-oximetry-derived perfusion indices [ 51 ] show only moderate discrimination for post-spinal hypotension but can help identify women at extremes of risk and fit easily into routine monitoring. Rather than relying on any single threshold, these indices are likely to be most useful as components of multivariable risk scores. 5. Procedural guidance for neuraxial and regional anesthesia and analgesia AI-enhanced ultrasound use has rapidly expanded in regional anesthesia, including peripheral and neuraxial blocks, offering both real-time procedural support and high-yield training applications [ 52 , 53 ]. AI assistance commonly involves automated segmentation of key structures with outlines or color overlays, guiding users to nerves, while highlighting vessels, pleura, and peritoneal spaces. These systems can improve image interpretation for nonexpert users, standardize views, and potentially reduce needle redirections and mechanical complications. For obstetric anesthesiologists, this technology is particularly relevant for epidural placement in patients with obesity, scoliosis, or prior spine surgery, where conventional surface landmarks are unreliable. Most published studies remain proof-of-concept or simulation-based but show favorable performances. Examples include deep-learning models which identify the interscalene brachial plexus more accurately than nonexperts [ 54 ], and real-time anatomy-identification tools assisting trainees in acquiring and labeling peripheral nerve block images [ 55 ]. In a simulation-based randomized trial of sciatic nerve block training, AI-assisted feedback reduced needle-induced paresthesia and improved perceptual learning compared with conventional teaching [ 56 ]. External validation using AI-generated color overlays demonstrated high accuracy of structure identification with low false-positive and false-negative rates, and suggests potential reductions in predicted block failure [ 57 ]. Scoping reviews synthesize this literature and emphasize heterogeneity of study designs, limited patient-outcome data, and the need for standardized evaluation before routine clinical deployment [ 52 ]. AI-assisted ultrasound is equally promising in neuraxial and paravertebral procedures. Thoracic paravertebral imaging studies report that automated identification of vertebral and pleural landmarks is generally “basically correct,” with accuracy comparable to expert reviewers [ 58 ]. For central neuraxial blockade, algorithms estimating the distance from skin to the posterior complex or epidural space and assist with vertebral level identification show good correlation with human measurements and >84% agreement on intervertebral level, with ≥93% accuracy for identifying lumbar interspace versus spinous process across most studies [ 52 ]. An obstetric-specific study of term parturients found that a ML–based lumbar ultrasound system (SLIDE) achieved substantially higher agreement with an expert ultrasound reference standard for L3–4 identification than manual palpation [ 59 ]. Early clinical reports also describe successful use of AI-enhanced handheld ultrasound to guide epidural placement in extremely obese parturients, illustrating real-world feasibility while promoting the need for larger, outcome-focused obstetric studies. 6. Peripartum pain AI is used to improve pain management during labor, delivery, and postpartum through predictive analytics, real-time monitoring, and immersive technologies like virtual reality (VR). Machine learning models trained on ECG-derived features can distinguish contraction-related labor pain from baseline with high accuracy, supporting the concept of objective, continuous pain monitoring [ 60 ]. In the postoperative setting, ML models developed for women undergoing cesarean delivery have predicted time to first use of patient-controlled intravenous analgesia and identified key determinants, including esketamine use, anesthesia technique, and anesthetic regimen, illustrating the potential for individualized analgesic protocols [ 61 ]. A separate framework for predicting postpartum chronic pain at six months used gradient boosting to integrate early postoperative pain scores and obstetric factors, highlighting the following risk markers: numerical rating scale at three days, pre-delivery body mass index, newborn weight, multiparity, and antepartum back pain [ 62 ]. Immersive technologies such as VR complement these predictive approaches. Randomized trials and a recent meta-analysis show that VR reduces labor pain intensity, anxiety, and fear of childbirth, while improving satisfaction compared with traditional care or no intervention [ 63 - 65 ]. Taken together, these six domains illustrate the current landscape of AI in obstetric anesthesia, with most applications remaining in early model development or external validation and only a minority approaching routine clinical deployment ( Fig. 1 ). Figure 1. Open in a new tab Translational maturity of AI and machine-learning applications in obstetric anesthesia. Each row represents a major clinical domain covered in this review; columns reflect stages along an implementation pipeline, from retrospective model development to external validation, prospective deployment, and integration into clinical workflows or regulatory frameworks. Filled markers indicate domains where multiple studies exist at that stage; open markers reflect emerging or very limited evidence. Most obstetric-specific applications remain in early development or external validation phases, with very few models evaluated in prospective impact studies or embedded into routine anesthetic workflows. Discussion Despite real progress in obstetric care, preventable maternal morbidity and mortality still occur, and the novel AI tools hold promise for earlier warning and more personalized care. Across postpartum hemorrhage, hypertensive disease, sepsis, hemodynamic instability, neuraxial procedures, and pain management, recent work demonstrates that AI and ML can improve maternal care beyond static risk scores and heuristic decision-making toward dynamic, data-driven early-warning and decision-support systems. Yet the gap between impressive pilot model performance and measurable clinical improvements in maternal outcomes remains substantial. In postpartum hemorrhage and placenta accreta spectrum, EHR-integrated and imaging-based models consistently outperform traditional risk-stratification tools and can identify high-risk patients earlier in pregnancy or at admission for delivery. Similar trends can be seen in maternal cardiovascular disease, where ML models that incorporate longitudinal EHR data, biomarkers, and, in some cases, genetic risk scores refine preeclampsia and peripartum cardiomyopathy risk and could inform timing of delivery, anesthetic technique, and postoperative surveillance. In maternal sepsis and fever, obstetric-specific early warning systems and parsimonious ML models show promise but are not reliable as stand-alone diagnostics, particularly given concerns about low sensitivity and alert fatigue. For hemodynamic instability, AI-enabled waveform analysis and noninvasive indices (e.g. perfusion index and Shock Index) offer early prediction of spinal-induced hypotension and may help tailor vasopressor strategies. Finally, AI-assisted ultrasound and ML-guided pain management, including immersive technologies such as virtual reality, highlight applications in image interpretation, procedure planning, and analgesia, although evidence is still dominated by small, single-center pilot and simulation-based studies. Current limitations temper enthusiasm. Most models are trained and tested in narrowly defined cohorts, with limited external validation, inconsistent outcome definitions, and sparse reporting of calibration or fairness metrics. Performance frequently degrades when models are generalized across institutions or when outcomes transition from research-grade to routine EHR data or from high-resource to resource-limited settings. In many domains, simpler models using a small number of well-chosen predictors can perform nearly as well as more complex architectures, suggesting that parsimony, interpretability, and ease of implementation may be more important than marginal gains in model evaluation metrics. Future studies should prioritize prospective, ideally pragmatic, evaluations that examine not just predictive performance but also changes in clinician behavior, workflow, and maternal–neonatal outcomes. Implementation barriers are equally important. Few tools seamlessly integrate into obstetric anesthesia workflows or major EHR systems, and local teams often lack the data science, governance, and informatics infrastructure needed for safe deployment, monitoring, and recalibration. Access to integration pathways remains controlled by EHR vendors or commercial AI suppliers, which can limit innovation and widen disparities between well-resourced centers and hospitals with limited technical resources. Addressing these challenges will require open, standards-based integration, shared evaluation frameworks, and collaborative development between clinicians, engineers, and vendors. Ethical, legal, and safety considerations are central to AI deployment strategies. At present, liability for AI-supported decisions rests with clinicians and institutions rather than model developers, underscoring the need for local oversight, education, and continuous post-implementation evaluation. Generative AI and large language models introduce specific model risks—including variability, sycophancy, and hallucinations—that make them unsuitable as unsupervised clinical decision-makers [ 66 ]. All ML models embed bias from their training data and unexamined use can exacerbate existing inequities in maternal care, whereas explicit assessment of performance across race, ethnicity, language, and socioeconomic strata is essential to potentially mitigate algorithmic discrimination [ 67 ]. Finally, cybersecurity and data-privacy concerns are paramount and any AI implementation must comply with local security regulations and institutional policies. For obstetric anesthesiologists, the near-term opportunity is to function as co-designers and critical evaluators of AI tools rather than passive recipients. This includes defining clinically meaningful outcomes, advocating for transparent and interpretable models, insisting on obstetric-specific validations, and participating in multicenter collaborations that can generate the large, diverse datasets required for robust modeling. If these technical, organizational, and ethical challenges are addressed, AI-enabled prediction, decision support, and procedural guidance have the potential to meaningfully reduce preventable maternal morbidity and mortality while enhancing the safety and personalization of anesthetic care. Conclusion AI and ML methodologies possess clear advantages in risk prediction, decision support, and procedural guidance throughout maternal care, especially within obstetric anesthesia. As these technologies mature, clinician-specific iterative feedback from real-world use, interdisciplinary collaboration, and adaptive regulation will be crucial to realize the full potential of AI-driven maternal safety and minimize the current risks. Key Points. AI tools for postpartum hemorrhage, placenta accreta spectrum, and hypertensive disease increasingly outperform traditional risk scores but remain limited by external validation and heterogeneous outcome definitions. Obstetric-specific early warning systems and parsimonious AI models for sepsis and intrapartum fever show promise but are not yet reliable as stand-alone diagnostics and risk contributing to alert fatigue. Waveform analytics, noninvasive hemodynamic indices, and AI-assisted ultrasound can anticipate hypotension and facilitate safer neuraxial and regional techniques, particularly in anatomically challenging patients. AI-based frameworks for labor and post-cesarean pain, together with virtual reality interventions, support individualized, multimodal analgesia in the peripartum period. Real-world benefit of AI in obstetric anesthesia will depend on interpretable design, equitable data and deployment, robust governance, and seamless EHR integration rather than model accuracy alone. Acknowledgements: During the preparation of this work, the authors used OpenAI ChatGPT (accessed September 2025) in order to assist with language polishing and consistency checks. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Funding statement: Supported by the NIH/NHLBI 1K08HL161326-01A1 (VPK), NIH/ODDS OT2OD038029 (VPK), Massachusetts Life Sciences Center (VPK), and NIH/NICHD R01 HD117802 (VPK). Abbreviations: AI Artificial intelligence AUC Area under the receiver operating characteristic curve BMI Body mass index CSL Consortium on Safe Labor ECG Electrocardiogram EHR Electronic health record EWS Early warning system FDA U.S. Food and Drug Administration HPI Hypotension Prediction Index ML Machine learning MRI Magnetic resonance imaging PARCCS Prediction of Acute Risk for Cardiovascular Complications in the Peripartum Period Score PAS Placenta accreta spectrum PPCM Peripartum cardiomyopathy PPH Postpartum hemorrhage qSOFA Quick Sequential Organ Failure Assessment SHAP SHapley Additive exPlanations SLIDE Spine Level Identification ultrasound system SOFA Sequential Organ Failure Assessment VR Virtual reality WHO World Health Organization Footnotes Conflicts of interest: VPK reports consulting fees from Avania CRO and PPD Thermo Fisher Scientific, unrelated to the current work and patent #WO2021119593A1 for the control of a therapeutic delivery system assigned to Mass General Brigham. 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