The promise of large language models in health care

A large language model for electronic health records

Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847

Artificial Intelligence in mental health and the biases of language based models

Straw, I., & Callison-Burch, C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0240376 · Cited by 164

Generative adversarial networks and synthetic patient data: current challenges and future perspectives

Arora, A., & Arora, A. (2022) · Future Healthcare Journal · doi:10.7861/fhj.2022-0013 · Cited by 161

SLIM: A Heterogeneous Accelerator for Edge Inference of Sparse Large Language Model via Adaptive Thresholding

Xu, W., Choi, H., Hsu, P.-K., Yu, S., & Simunic, T. (2025) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3750727 · Cited by 3

Interventions that strengthen the patient-clinician relationship improve healthcare outcomes: An updated systematic review and meta-analysis

Neal, D. P., Nestor, B. A., Archer, C., Ulate, M. M., Wild, M. G., & Kelley, J. M. (2026) · Patient Education and Counseling · doi:10.1016/j.pec.2026.109699 · Cited by 3

Requirements Engineering for LLM: The Case of Digital Inquiries Application

Solomon, A., Levy, M., Cohen, D. A., Younis, M., & Moshe, E. (2024) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew61692.2024.00058 · Cited by 3

Can Large Language Models Support Medical Facilitation Work? A Speculative Analysis

Abdulhamid, N. G., Ochieng, M., Bali, K., Ankrah, E. A., Karusala, N., Ronen, K., & O’Neill, J. (2023) · African Human Computer Interaction Conference (AfriCHI) · doi:10.1145/3628096.3628752 · Cited by 7

Generative AI in Pandemic Prediction

Tavana, P., & ZareiNejad, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-82963-5_2 · Cited by 3

A Comparative Analysis of GPT-3.5, GPT-4, GPT–4 Omni, Gemini Advanced, and Gemini 1.5 in Answering Frequently Asked Questions Regarding High Tibial Osteotomy

Kim, S. H., Shin, J.-S., Lee, H., Shin, S. Y., Kang, K. M., & Song, S. Y. (2025) · Orthopaedic Journal of Sports Medicine · doi:10.1177/23259671251385127 · Cited by 3

Behavior Change Content and Implementation of Large Language Model–Driven Conversational Agents in Cardiometabolic Care: Scoping Review

Zhao, Y., Guo, R., Miao, Y., Luo, Y., Wang, H., & Wu, Y. X. (2026) · Journal of Medical Internet Research · doi:10.2196/89190 · Cited by 2

Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians

Cho, A.-Y., Eo, S., Jeon, M., Kim, J., Ihm, J., & Seo, D. (2026) · JMIR Medical Education · doi:10.2196/91809 · Cited by 2

Automatically pre-screening patients for the rare disease aromatic l -amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population

Cohen, A. M., Kaner, J., Miller, R., Kopesky, J. W., & Hersh, W. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad244 · Cited by 2

Transforming Mortality Prediction: A Transformer-Based Mortality Prediction Model

Weiss, J., Azhir, A., Ram, N., & Rehkopf, D. H. (2025) · The Journals of Gerontology Series B · doi:10.1093/geronb/gbaf089 · Cited by 2

Incorporating Medical Assistants in eHealth Environments Using an Agentic RAG Approach

Kalathas, D., Menychtas, A., Maglogiannis, I. G., & Tsanakas, P. D. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96235-6_12 · Cited by 4

Differential reasoning and chain-of-thought processes in Deepseek-R1 and Open AI o3-mini-high for determining American Society of Anesthesiologists physical status

Ke, Y. H., Leong, Y. H., Jin, L., Elangovan, K., Abdullah, H. R., Sia, A. T. H., Ong, J. C. L., Shah, N. H., Wong, T. Y., & Ting, D. S. W. (2025) · British Journal of Anaesthesia · doi:10.1016/j.bja.2025.05.044 · Cited by 2

Perspective on patient and non-academic partner engagement for the responsible integration of large language models in health chatbots

Jaiswal, N., Ma, Y., Lebouché, B., Poenaru, D., Pomey, M., Achiche, S., Lessard, D., Engler, K., Montiel, Z., Acevedo, H., et al. (2025) · Research Involvement and Engagement · doi:10.1186/s40900-025-00804-1 · Cited by 1

Assessing and Comparing Free Large Language Models’ Responses to a Clinical Case: Accuracy, Safety, and Reliability

Sblendorio, E., Cascio, A. L., Napolitano, D., Germini, F., Dentamaro, V., Piredda, M., & Cicolini, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-89704-7_11 · Cited by 2

The convergence of large language models and physician–patient relationship research: a bibliometric analysis

Chen, A., & ZHANG, X. (2026) · Asian Journal of Medical Humanities · doi:10.1515/ajmedh-2025-0035 · Cited by 1

Preliminary evaluation of DeepSeek-R1 and GPT-5.3 in selected PET/CT clinical scenarios: patient preparation, report interpretation, and diagnostic reasoning

Duan, R., Pang, J., Zheng, L., Guo, Z., Li, T., 边艳珠, & Hu, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1785671 · Cited by 1

Retrieval-augmented reasoning reshapes collective reliability in text-based radiology question answering

Farajiamiri, M., Sopa, J., Afza, S., Adams, L. C., Ordonez, F. B., Nguyen, T.-T., Lotfinia, M., Wind, S., Bressem, K. K., Nebelung, S., et al. (2026) · Patterns · doi:10.1016/j.patter.2026.101639 · Cited by 2

COMPARISON OF PERFORMANCES OF OPEN ACCESS NATURAL LANGUAGE PROCESSING BASED CHATBOT APPLICATIONS IN TRIAGE DECISIONS

Sarbay, İ., Berikol, G. B., Özturan, İ. U., & GRİMES, K. (2023) · Kırıkkale Üniversitesi Tıp Fakültesi Dergisi · doi:10.24938/kutfd.1369468 · Cited by 1

Zero-Shot Learning Capabilities of Large Language Models (LLMs) in Extracting Key Insights from Suicidal Narratives

Azade, R. S., & Sazzed, S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11400765 · Cited by 1

One is Not Enough: Multi-Agent Conversation Framework Enhances Rare Disease Diagnostic Capabilities of Large Language ModelsPreprint

Li, J., Chen, X., Liu, W., Li, W., Guo, Y., You, M., Chen, G., & Li, K. (2023) · Research Square · doi:10.21203/rs.3.rs-3757148/v1 · Cited by 1

Large Language and Generative Foundation Models for Cloud-Enabled Medical Imaging and Internet of Medical Things: A PRISMA Systematic Review of Architectures, Security, and DeploymentPreprint

Kalejahi, B. K., Khalimova, K., & Rajabi, M. J. (2026) · Research Square · doi:10.21203/rs.3.rs-8859031/v1 · Cited by 1

Using artificial intelligence in medical research: Challenge or opportunity?

Wang, X., & Li, J. (2023) · Asian Journal of Surgery · doi:10.1016/j.asjsur.2023.05.102

Proposing a person-centred decentralised health data ecosystem to optimise applied data science and artificial intelligence for dementia prevention and cognitive longevity. (Preprint)Preprint

Albertyn, C. P., Surodina, S., Tan, B., Woods, T., Corner, L., Aarsland, D., & Siow, R. C. M. (2023) · doi:10.2196/preprints.52111

How large language models can be used in the field of orthodontics

Khurdal, A., Mani, S., Toshniwal, N., & Paul, A. (2023) · Journal of Contemporary Orthodontics · doi:10.18231/j.jco.2023.042

Five Essential Questions for Clinical NLP Systems Developers from the Lens of AI GovernancePreprint

Bozkurt, S., McElfresh, D. C., Lindvall, C. J., & Tamang, S. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4597348

Real-World Performance of Large Language Models in Emergency Department Chest Pain Triage and the development of MediGuide-14BPreprint

Tang, Y. (2024) · Research Square · doi:10.21203/rs.3.rs-4785726/v1

Overview of the Evaluation Task for Syndrome Differentiation Thought in Traditional Chinese Medicine in CHIP 2024

Hao, M., Yao, K., Huang, Y., Yang, L., Peng, S., Wang, Z., & Zhu, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4298-4_1

Teaching Clinical Reasoning in the Age of AI: A Mixed-Methods Formative Evaluation of AI-Generated Script Concordance Tests and Expert Embodiment (Preprint)Preprint

Hudon, A., Phan, V., Charlin, B., & Wittmer, R. (2025) · doi:10.2196/preprints.76618

Large Language Models for the assessment of medical students’ clinical decision-makingPreprint

Benker, S. C., Vollprecht, J., Papan, C., Lu, M. H., & Darici, D. (2025) · Research Square · doi:10.21203/rs.3.rs-6660928/v1

A generalized LLMs framework to support public health financing through probabilistic predictions and uncertainty quantification

Guariso, D., Adewoyin, R. A., Aguilar, G. R., Guerrero, O. A., & Davies, A. R. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103203

Multi-Institution AI Security in Federated Drug Design Systems.

Ezeogu, F. L. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1408000052

Patient Experiences with Diagnostic Revision in Lymphoma: Analysis of Chinese Online Forum Narratives Using Natural Language ProcessingPreprint

He, F., Yang, Z., Zhang, X., Koh, W. L., Seng, L. L., Goodwin, N., & Valderas, J. M. (2025) · medRxiv · doi:10.1101/2025.09.07.25335273

Use of Large Language Models for Simulating Motivational Interviewing Counselling: A Scoping Review ProtocolPreprint

Akinrolie, O., Fatai, K. E., Askin, N., Onyekwelu, A. I., Ekowa, J. L., Kolawole, F. O., Akinrolie, S., & Fawole, H. O. (2025) · Research Square · doi:10.21203/rs.3.rs-7539260/v1

The Use of LLMs in the Legal Design Process—Designing Effective Access to Health Data Under the EHDS and the GDPR

Kovalainen, J., & de Heer, S. (2025) · Perspectives in law, business and innovation · doi:10.1007/978-981-95-2058-9_12

Editorial: AI for health behavior change

Mogles, N. M., Joel-Edgar, S., Zhang, C., & Klein, M. C. A. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1735279

Proposing a person-centred health data ecosystem framework to optimise digital innovation and artificial intelligence for dementia prevention and cognitive longevity

Albertyn, C. P., Surodina, S., Świeboda, P., Tan, B., Woods, T., Corner, L., Aarsland, D., & Siow, R. C. M. (2025) · Longevity · doi:10.1080/30653495.2025.2570721

Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis

Heidari, Z., Mirghorbani, M., Abounoori, M., Ebrahimibesheli, K., Tabarestani, M., Khabazkhoob, M., Yousefi, S., & Modjtahedi, B. S. (2026) · International Ophthalmology · doi:10.1007/s10792-026-03960-9

Comparative analysis of Chinese large language model performance on atrial fibrillation questions

Liu, G., Cheng, K., Xu, Y., Pang, Y., Ling, Y., Chen, Q., Zhu, W., & Junbo, G. (2026) · BMC Cardiovascular Disorders · doi:10.1186/s12872-026-05566-6

Evaluation of Chat Generative Pretrained Transformer’s Performance in Cosmetic Injection Consultation by Authoritative Plastic Surgeons

Hu, J., Wei, Q., Li, J., Lu, S., Zhuang, J., Xia, Y., He, M.-Y., Xian, W., & Chen, F. (2026) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000007479

Evaluating the Few-Shot Performance of Large Language Models for Classifying Clinical Risk Factors in Mental Health Text

Sazzed, S., Azade, R. S., Dehan, F. N., & Pial, M. E. R. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11400847

Discovering the Significance of LLM’s in Medical Science for Disease Detection

Vyas, B. R., Desai, M. N., Patel, H., Mahavar, A., & Panchal, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-22062-2_10

PhysB: a framework for designing conversational agents to support physical activity in adolescents

Moore, R., Al-Tamimi, A.-K., & Lazuras, L. (2026) · Journal of Health Population and Nutrition · doi:10.1186/s41043-026-01254-5

Artificial Intelligence in Primary Healthcare: Integration, Pathways, Benefits, and Challenges (Preprint)Preprint

Xu, S. (2026) · doi:10.2196/preprints.104385 · Cited by 1

PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration

Liu, Y., Han, W., Cai, C., Yuan, X., & Wang, C. (2026) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom59046.2026.11571688

Balancing Innovation and Integrity in the Age of AI: Using Design Thinking to Guide Students in Meaningful AI Usage While Preventing Over-Reliance

Chen, H., Liao, Y.-S., Liu, C., & Chiou, W. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29900-0_13

Quality, completeness, readability, and response time of AI chatbots in the management of deep caries and pulp exposure: a bilingual comparative study

Karakoyunlu, E., Mıdoglu, G., Gürler, K., Öztürk, Y. E. G., & Yılmaz, K. (2026) · BMC Oral Health · doi:10.1186/s12903-026-09298-z

Case-matched retrieval improves textual alignment of LLM-generated radiology impressions

Sorin, V., Collins, J. D., Hahn, L. D., Bratt, A. K., Klang, E., & Korfiatis, P. D. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354688

Decoding high-order clinical correlations: a knowledge-driven large language model framework for specialized medical decision-making

Li, Q., Li, Y., Ye, C., Zhang, K., Bai, J., Liu, X., Li, Z., Meng, X., Min, X., & Guo, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1772518

Large Language Models in Clinical Contexts: Infrastructure, Oversight, and Risk Dynamics

Müller, A., Weber, S., Hoffmann, J., Schneider, L., & Klein, T. (2025) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/j510298368

The effects of artificial intelligence-based chatbots on mental health: A systematic review and three-level meta-analysis

Wang, Q., Zhong, J., Qi, K., Bao, H., Wang, Y., Guo, S., & Chen, A. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117459

Domain Adaptation of a Large Language Model for Radiology Impression Generation – Multi-Reader Evaluation

Sorin, V., Reynolds, M., Hara, A. K., Gupta, A. C., Collins, J. D., Jensen, Z. D., Vassilieva, N., Wolfe, J., Rezaei, S., Callstrom, M. R., et al. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02484-0

Large language models in health care: Development, applications, and challenges

Yang, R., Tan, T. F., Lu, W., Thirunavukarasu, A. J., Ting, D. S. W., & Liu, N. (2023) · Health Care Science · doi:10.1002/hcs2.61 · Cited by 366

Large language models in biomedicine and health: current research landscape and future directions

Lu, Z., Peng, Y., Cohen, T., Ghassemi, M., Weng, C., & Tian, S. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae202 · Cited by 92

Generative AI and large language models in health care: pathways to implementation

Raza, M. M., Venkatesh, K. P., & Kvedar, J. C. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00988-4 · Cited by 121

Enhancing Health Care Communication With Large Language Models—The Role, Challenges, and Future Directions

Subramanian, C. R., Yang, D. A., & Khanna, R. R. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.0347 · Cited by 47

A Framework for Critically Assessing ChatGPT and Other Large Language Artificial Intelligence Model Applications in Health Care

Ilicki, J. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.03.006 · Cited by 20