Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 9 appraisals
Bulletin of the World Health Organization
Digital epidemiology investments, Saudi Arabia
PROBLEM: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. APPROACH: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. LOCAL SETTING: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. RELEVANT CHANGES: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. LESSONS LEARNT: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making. PROBLÈME: Les méthodes traditionnelles de surveillance épidémiologique sont souvent limitées par des retards dans la transmission des données et par la fragmentation des systèmes de données. L’Arabie saoudite est confrontée à des défis supplémentaires en matière de santé publique liés aux rassemblements de masse lors du Hajj et de la Omra, au fardeau croissant des maladies non transmissibles et à l’urbanisation rapide, ce qui souligne la nécessité d’investir dans l’épidémiologie numérique. APPROCHE: L’Arabie saoudite a accéléré la transformation numérique dans le domaine des soins de santé grâce aux initiatives de la Vision 2030. Ces stratégies comprennent des plateformes d’échange d’informations de santé, des analyses alimentées par l’intelligence artificielle, des services de télémédecine et des systèmes de surveillance numérique employés pendant le Hajj. La présente étude passe en revue les initiatives actuelles visant à investir dans l’épidémiologie numérique en Arabie saoudite, les défis liés à leur mise en œuvre et les priorités politiques. ENVIRONNEMENT LOCAL: Le système de santé saoudien fonctionne selon un modèle majoritairement public. Le ministère de la Santé est le principal prestataire, régulateur et bailleur de fonds de la plupart des services de santé. La couverture des soins de santé est pratiquement universelle, les citoyens bénéficiant de services gratuits au travers du système public. Les réformes en cours visent à décentraliser progressivement certaines fonctions. CHANGEMENTS SIGNIFICATIFS: Les initiatives menées dans le cadre de la Vision 2030 ont favorisé la surveillance des maladies, l’intégration des données et le renforcement des capacités d’intervention de santé publique. Les réformes existantes en matière de santé numérique ont jeté les bases nécessaires à l’intégration de l’épidémiologie numérique dans les pratiques courantes de santé publique. Toutefois, des défis subsistent, notamment le manque d’interopérabilité entre les établissements, la pénurie de main-d’œuvre, les inégalités d’accès au numérique, ainsi que des préoccupations en matière de gouvernance des données, de confidentialité et de biais dû aux algorithmes. LEÇONS TIRÉES: L’expérience de l’Arabie saoudite suggère une plus grande efficacité de l’épidémiologie numérique lorsque celle-ci s’inscrit dans le cadre de réformes plus larges en matière de santé numérique. La réussite de la mise en œuvre nécessite non seulement une infrastructure numérique, mais aussi le développement des ressources humaines, une gouvernance éthique, la transparence ainsi que des mécanismes d’intégration des données numériques dans la prise de décision en matière de santé publique. SITUACIÓN: Los métodos tradicionales de vigilancia epidemiológica suelen verse limitados por retrasos en la notificación y por sistemas de datos fragmentados. Arabia Saudí se enfrenta además a desafíos de salud pública derivados de las concentraciones masivas de personas durante el Hach y la Umrah, del aumento de la carga de las enfermedades no transmisibles y de la rápida urbanización, lo que destaca la necesidad de invertir en epidemiología digital. ENFOQUE: Arabia Saudí ha acelerado la transformación digital de la atención sanitaria mediante las iniciativas de la Visión 2030. Las estrategias incluyen plataformas de intercambio de información sanitaria, análisis basados en inteligencia artificial, servicios de telemedicina y sistemas digitales de vigilancia utilizados durante el Hach. Se examinan las iniciativas actuales de inversión en epidemiología digital en Arabia Saudí, los desafíos para su implementación y las prioridades en materia de políticas. MARCO REGIONAL: El sistema sanitario de Arabia Saudí funciona bajo un modelo predominantemente público. El ministerio de salud es el principal proveedor, regulador y financiador de la mayoría de los servicios sanitarios. La cobertura sanitaria es prácticamente universal y la ciudadanía recibe servicios gratuitos a través del sistema público. Las reformas en curso tienen por objeto descentralizar gradualmente determinadas funciones. CAMBIOS IMPORTANTES: Las iniciativas de la Visión 2030 han fortalecido la vigilancia de enfermedades, la integración de datos y la capacidad de respuesta en salud pública. Las reformas existentes en materia de salud digital han creado una base para integrar la epidemiología digital en la práctica habitual de la salud pública. No obstante, persisten desafíos, entre ellos la interoperabilidad fragmentada entre instituciones, la escasez de personal, las desigualdades en el acceso digital y las preocupaciones relacionadas con la gobernanza de los datos, la privacidad y los sesgos algorítmicos. LECCIONES APRENDIDAS: La experiencia de Arabia Saudí sugiere que la epidemiología digital es más eficaz cuando se integra en reformas más amplias de salud digital. Su implementación satisfactoria requiere no solo infraestructura digital, sino también desarrollo de capacidades del personal, gobernanza ética, transparencia y mecanismos para incorporar los datos digitales a la toma de decisiones en salud pública. المشكلة: غالبًا ما تكون طرق الرصد الوبائي التقليدية محدودة بسبب التأخيرات في الإبلاغ وأنظمة البيانات المشتتة. وتواجه المملكة العربية السعودية تحديات إضافية في مجال الصحة العامة نتيجة للتجمعات الكبيرة خلال موسمي الحج والعمرة، وتزايد عبء الأمراض غير المعدية، والتوسع الحضري السريع، مما يُبرز الحاجة إلى الاستثمار في علم الأوبئة الرقمي. الأسلوب: سارعت المملكة العربية السعودية في التحول الرقمي في مجال الرعاية الصحية من خلال مبادرات رؤية 2030. وتشمل هذه الاستراتيجيات منصات تبادل المعلومات الصحية، والتحليلات المدعومة بالذكاء الاصطناعي، وخدمات العلاج الطبي عن بُعد، وأنظمة المراقبة الرقمية المستخدمة خلال موسم الحج. نستعرض في هذا البحث المبادرات الحالية للاستثمار في علم الأوبئة الرقمي في المملكة العربية السعودية، وتحديات التنفيذ، وأولويات السياسة الحكومية. المواقع المحلية: يعمل النظام الصحي في المملكة العربية السعودية بموجب نموذج حكومي بشكل رئيسي. وتُعد وزارة الصحة هي المزود الرئيسي، والجهة المنظمة، والممول لمعظم خدمات الرعاية الصحية. وتعد تغطية الرعاية الصحية شاملة تقريبًا، حيث يحصل المواطنون على الخدمات مجانًا من خلال النظام الحكومي. وتهدف الإصلاحات الجارية إلى تطبيق اللامركزية تدريجيًا على وظائف محددة. التغيّرات ذات الصلة: إن المبادرات بموجب رؤية 2030 تدعم رصد الأمراض، وتكامل البيانات، وقدرات الاستجابة في مجال الصحة العامة. لقد أدت إصلاحات الصحة الرقمية إلى إنشاء أساس لدمج علم الأوبئة الرقمي في ممارسات الصحة العامة الروتينية. ومع ذلك، لا تزال هناك تحديات، تشمل التشغيل المشترك المشتت بين المؤسسات، ونقص الكوادر، والوصول غير العادل إلى البيانات الرقمية، والمخاوف المتعلقة بحوكمة البيانات، والخصوصية، والتحيز الخوارزمي. الدروس المستفادة: تشير تجربة المملكة العربية السعودية إلى أن علم الأوبئة الرقمي يكون أكثر فعالية عند دمجه ضمن إصلاحات أوسع نطاقًا في مجال الصحة الرقمية. ويتطلب التنفيذ الناجح ليس فقط بنية تحتية رقمية، بل أيضًا تطوير القوى العاملة، والحوكمة الأخلاقية، والشفافية، وآليات دمج البيانات الرقمية في عملية صنع القرار في مجال الصحة العامة. 问题: 传统流行病学监测手段常受制于报告滞后与数据系统碎片化的问题。沙特阿拉伯还面临朝觐与副朝期间大型聚众活动、非传染性疾病负担攀升以及快速城市化带来的额外公共卫生难题，凸显出投资发展数字流行病学的必要性。. 方法: 沙特阿拉伯依托《2030 愿景》(Vision 2030) 相关举措，加快推进医疗健康领域数字化转型。相关举措包括医疗信息交互平台、人工智能驱动的数据分析、远程医疗服务以及朝觐期间启用的数字化监测系统。本文梳理了沙特阿拉伯的当前数字流行病学投资项目、项目实施方面的挑战及政策优先发展方向。. 当地状况: 沙特阿拉伯的医疗卫生体系以公立运营模式为主。卫生部是大多数医疗护理服务的主要提供方、监管机构和出资主体。医疗卫生保障基本实现全民覆盖，本国公民可通过公立医疗体系免费就医。现行改革旨在逐步下放部分职能。. 相关变化: 《2030 愿景》框架下的各项举措，助力完善疾病监测、数据整合与公共卫生应急处置能力建设。现有的数字医疗改革为将数字流行病学融入常规公共卫生工作奠定了基础。然而，仍存在一些挑战：医疗机构间数据互通割裂、人才短缺、数字资源获取不均衡，以及数据治理、隐私安全与算法偏见等隐患问题。. 经验教训: 沙特阿拉伯的实践经验表明，将数字流行病学融入大范围数字医疗改革体系中，其实施成效更佳。实施成功不仅需要数字化基础设施，还需人才队伍建设、合规伦理治理、信息公开制度，以及把数字化数据纳入公共卫生决策的配套机制。. ПРОБЛЕМА: Традиционные методы эпидемиологического надзора часто ограничены задержками в предоставлении отчетности и фрагментированностью систем данных. Саудовская Аравия сталкивается с дополнительными проблемами общественного здравоохранения, связанными с массовыми скоплениями людей во время хаджа и умры, растущим бременем неинфекционных заболеваний и стремительной урбанизацией. Все это подчеркивает необходимость инвестиций в цифровую эпидемиологию. ПОДХОД: Саудовская Аравия ускорила цифровую трансформацию здравоохранения в рамках инициатив программы Vision 2030. Среди ключевых направлений – платформы для обмена медицинской информацией, аналитические системы на основе искусственного интеллекта, телемедицинские сервисы и цифровые системы мониторинга, используемые во время хаджа. Авторы рассматривают текущие инвестиционные инициативы в области цифровой эпидемиологии в Саудовской Аравии, проблемы внедрения и приоритетные направления политики. МЕСТНЫЕ УСЛОВИЯ: Система здравоохранения Саудовской Аравии опирается преимущественно на государственную модель. Министерство здравоохранения является основным поставщиком медицинских услуг, регулятором и главным источником финансирования большинства медико-санитарных услуг. Охват медицинской помощью практически всеобщий: граждане получают услуги бесплатно через государственную систему. Текущие реформы направлены на постепенную децентрализацию отдельных функций. ОСУЩЕСТВЛЕННЫЕ ПЕРЕМЕНЫ: Инициативы в рамках программы Vision 2030 способствовали развитию эпидемиологического надзора, интеграции данных и укреплению потенциала реагирования системы общественного здравоохранения. Проводимые реформы в области цифрового здравоохранения заложили фундамент для внедрения цифровой эпидемиологии в повседневную практику общественного здравоохранения. Однако сохраняются проблемы, включая недостаточную совместимость информационных систем разных учреждений, нехватку кадров, неравномерный доступ к цифровым технологиям, а также вопросы управления данными, конфиденциальности и алгоритмической предвзятости. ВЫВОДЫ: Опыт Саудовской Аравии показывает, что цифровая эпидемиология наиболее эффективна, когда интегрирована в более широкие реформы цифрового здравоохранения. Для успешного внедрения необходима не только цифровая инфраструктура, но и развитие кадрового потенциала, этическое регулирование, прозрачность и механизмы интеграции цифровых данных в процессы принятия решений в сфере общественного здравоохранения.
2 Aug 2026
Read appraisal →Medicine
A bibliometric analysis of global trends in AI-driven digital health technologies for diabetes management
BACKGROUND: Digital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions. METHODS: We collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords. RESULTS: A total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." "diabetes" was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening. CONCLUSIONS: This study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.
26 July 2026
Read appraisal →International journal for equity in health
A conceptual framework for measuring AI health equity
BACKGROUND: Artificial intelligence (AI) is increasingly embedded in health systems globally and has the potential to improve efficiency, diagnostic accuracy, and decision support. However, its benefits remain unevenly distributed, particularly in low- and middle-income countries (LMICs). Models developed using datasets from specific populations may perform poorly in other settings, reinforcing structural inequities rather than correcting them. OBJECTIVE: This viewpoint proposes a composite framework, the AI in Healthcare Equity Index (AIHEI), to support measurable assessment of equity in health AI systems. FRAMEWORK: The AIHEI is designed to assess equity across five domains: data representation, algorithmic fairness, transparency and explainability, governance and oversight, and community impact and benefit sharing. By generating a standardised score, the index could enable comparisons across technologies, incentivise improvement, and support regulation, procurement, publication, and funding decisions. IMPLICATIONS: Pilots across diverse health domains and geographic settings are needed to assess feasibility, refine domain weighting, and evaluate reliability, reproducibility, and validity. Important challenges include contextual definitions of fairness, data sovereignty, post-deployment monitoring, and the risk of metric gaming. CONCLUSIONS: Quantifying equity in health AI is essential to ensure that AI does not create, widen, or exacerbate existing disparities by neglecting underserved populations. A common, objective measure of AI-related health equity can help move the field from ethical aspiration toward measurable accountability, monitoring, and enforcement.
25 July 2026
Read appraisal →Scientific reports
Differentially private federated learning for localized control of infectious disease dynamics
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localized strategy based on the German counties and communities managed by the related local health authorities (LHA). For the preservation of privacy to not oppose the availability of detailed situational data, we propose a privacy-preserving forecasting method that can assist public health experts and decision makers. ML methods with federated learning (FL) train a shared model without centralizing raw data. Considering the counties, communities or LHAs as clients and finding a balance between utility and privacy, we study a FL framework with client-level differential privacy (DP). We train a shared multilayer perceptron on sliding windows of recent case counts to forecast the number of cases in the future, while clients exchange only norm-clipped updates and the server aggregates updates with DP noise. We evaluate the approach on COVID-19 data on county-level during two phases: November 2020 and March 2022 (Omicron). As expected, very strict privacy ([Formula: see text]) yields unstable, unusable forecasts. At a moderately strong but still privacy-preserving level ([Formula: see text]), the DP model closely approaches the non-DP model: [Formula: see text] (vs. 0.96) and mean absolute percentage error (MAPE) [Formula: see text] in November 2020; [Formula: see text] (vs. 0.90) and MAPE [Formula: see text] in March 2022. Overall, our results support the feasibility of privacy-preserving collaboration among health authorities for local forecasting. In the evaluated COVID-19 phases, client-level DP-FL delivered useful county-level predictions with formal privacy guarantees under the stated threat model. The appropriate privacy budget should nevertheless be re-evaluated for other epidemic phases and applications.
8 July 2026
Read appraisal →The Tohoku journal of experimental medicine
At-Home Deaths and Regional Factors in Japan
In Japan, at-home deaths are not always popular, yet many people hope to spend their end-of-life at home, which makes this a socio-medical issue. Regional factors are associated with at-home deaths, but their relative importance has not been known. The present study investigated the relationship between at-home deaths and regional factors and ranked their importance at the municipal level in Japan. We used national open data on at-home deaths, population, medical resources, causes of death, economic factors and geographic factors as well as built the prediction model for at-home deaths using Extreme Gradient Boosting (XGBoost) regression. The Shapley Additive Explanations (SHAP) value was applied to rank the importance of regional factors and explain the XGBoost model. As a result, the median prevalence of at-home deaths across municipalities was 12.7%. A higher prevalence of at-home deaths was observed in large metropolitan areas such as Tokyo, Osaka, and Nagoya. SHAP values revealed that latitude was the most important factor in rank, followed by the number of clinics providing end-of-life care, longitude, population density, and population change rate. In conclusion, geographic and population-related factors were ranked highly among regional factors associated with at-home deaths. These findings may be informative for community planning to realize at-home deaths nationwide.
7 July 2026
Read appraisal →PloS one
Forecasting COVID-19 new cases using NBEATS deep learning and mobility data
COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from Google, Apple, and Our World in Data (OWID) were used in this study. Incorporating Google and Apple mobility data as covariates enhances both the accuracy and interpretability of the N-BEATS model. Our results show that N-BEATS consistently outperforms LSTM-Markov across all datasets and countries, consistently yielding lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, the N-BEATS model with covariates outperforms its counterpart without covariates, indicating that mobility data provide substantial value for forecasting new COVID-19 cases. Overall, this study demonstrates the effectiveness of the N-BEATS architecture in capturing pandemic dynamics and offers valuable insights for policymakers and public health officials in managing future outbreaks.
30 June 2026
Read appraisal →Journal of medical Internet research
Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review
BACKGROUND: Effective health care communication is crucial in the medical field. However, effective communication in clinical practice still faces numerous obstacles, and large language models (LLMs) offer various possibilities for improving the quality of medical communication. To date, there are no published reviews on the use of LLMs in health care communication. OBJECTIVE: This review sought to summarize the applications and challenges of LLMs in health care communication and to identify directions for future research. METHODS: A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library from January 2018 to November 2025. The search and selection process followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline and the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) checklist. Eligible studies used LLMs to facilitate health care communication among the public, patients, and clinicians. Following rigorous data extraction and cross-checking, we conducted a quantitative analysis of characteristics of the included literature. Furthermore, using communication accommodation theory as a framework, we identified application patterns of LLMs in health care communication and summarized current challenges and future directions. RESULTS: Ninety-six studies were included in this review, all published between 2023 and 2025, summarizing 4 patterns of LLM application in health care communication: transforming medical information (n=30), facilitating dynamic interaction (n=38), empowering communication capabilities (n=10), and optimizing clinical workflows (n=18). The role of LLMs in health care communication is undergoing a paradigm shift from "static information processing" to "dynamic intelligent interaction." Although they show great promise for practical applications, current evaluation methods and dimensions exhibit significant heterogeneity. Furthermore, LLMs still face multiple challenges in their practical application in health care communication, including technical reliability issues, social trust and adoption, interaction and access barriers, and clinical integration challenges. CONCLUSIONS: Unlike previous studies that merely touched upon the challenges and future directions, this scoping review uses communication accommodation theory to systematically map the application patterns and developmental landscape of LLM-mediated health care communication. Health care communication powered by LLMs holds significant innovation potential and is currently still in the early stages of rapid development. Future research should focus on optimizing model performance, strengthening ethical governance frameworks, enhancing human-machine collaboration models, and ensuring responsible application of LLMs in health care through rigorous empirical validation.
28 June 2026
Read appraisal →Journal of medical Internet research
Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance
Personal health large language models (PH-LLMs) are patient-facing conversational systems that synthesize user-entered information, patient-generated health data, wearable data, and selected personal health records-where users choose to connect them-into personalized, longitudinal, action-oriented health narratives. Unlike generic health chatbots that mainly provide one-off responses to isolated questions, PH-LLMs may generate continuing interpretations, priorities, and candidate next steps that patients bring into clinical encounters. In contrast to electronic health record-tethered clinical artificial intelligence (AI), they often originate outside institutional oversight and may be selected, used, or trusted by patients before professional review. This viewpoint examines how PH-LLMs may reshape the negotiation of medical authority by contributing to a shift from the traditional dyadic clinician-patient relationship toward a triadic model of negotiated authority, in which clinicians may increasingly need to mediate among clinical evidence, patient values, and algorithmic narratives. PH-LLMs may support patient participation by organizing symptoms, contextualizing home-monitoring and wearable data, improving health literacy, assisting chronic disease self-management, and preparing patients for more collaborative visits. Patient-facing AI narratives may also introduce distinct risks. At the individual level, these include inaccurate or incomplete responses arising from imprecise queries or missing context, misinterpretation of otherwise accurate information in the absence of clinical context, and contextually biased or poorly matched advice across demographic, cultural, linguistic, disability-related, or socioeconomic contexts. At the system level, they include authority conflict when AI recommendations diverge from clinical judgment, fragmentation of clinical truth, privacy and data-governance concerns, diffusion of accountability when harm results from advice produced outside clinical governance, and inequitable access to premium tools and continuous monitoring devices. To address these challenges, we propose a 3-layer clinical governance framework for patient-brought PH-LLM narratives. The first layer, evidence and provenance, makes AI-generated narratives epistemically legible by clarifying platform identity, data sources, temporal anchoring, uncertainty, and privacy-relevant data-use and retention conditions. The second layer, clinical arbitration and workflow integration, uses risk-stratified intake, proportionate documentation, escalation triggers, and equity-preserving workflows to embed PH-LLM outputs into routine care. The third layer, competence and accountability, defines the communication competencies, AI literacy supports, institutional responsibilities, vendor accountability, and risk-proportionate verification duties needed for triadic care. This framework is a conceptual and governance-oriented proposal rather than a validated clinical protocol. Future empirical work should evaluate its feasibility, documentation burden, equity effects, clinical safety impact, and acceptability among patients, clinicians, and health systems. Governed through these interdependent layers, PH-LLMs may serve as supporting infrastructure for safer, person-centered longitudinal care.
26 June 2026
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Solidarity or Segregation? ChatGPT Health and US Health Care Disparities.
ChatGPT Health, an artificial intelligence feature launched by OpenAI in January 2026, integrates personal medical records and consumer health data into a large language model-based chatbot. It positions itself as a digital health tool to help users navigate a fragmented US health care system marked by inaccessible insurance, medical deserts, and workforce shortages. We argue that, rather than closing these gaps, ChatGPT Health risks widening what we call the solidarity gap in health care. By appearing to address unmet needs, it may obscure and entrench the structural conditions that produce health care disparities. We identify specific risks to patient safety, such as the legitimization of dangerous self-rationing and self-medication behaviors, inaccurate triage of clinical emergencies, erosion of effective health communication, and the reinforcement of confirmation and anchoring bias. These risks are particularly acute for individuals facing structural barriers to care. To prevent ChatGPT Health and similar large language model-based health tools from deepening inequities, we propose solidarity-based policy interventions such as upstream reforms that rebuild the institutional foundations of access and health equity and downstream safeguards that embed artificial intelligence tools within accountable, equitable health care infrastructures as complements to rather than substitutes for human care.
24 June 2026
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