ChatGPT and Generating a Differential Diagnosis Early in an Emergency Department Presentation

Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738

The Association Between Length of Emergency Department Boarding and Mortality

Singer, A. J., Thode, H. C., Viccellio, P., & Pines, J. M. (2011) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2011.01236.x · Cited by 662

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

van Leeuwen, K. G., Schalekamp, S., Rutten, M. J. C. M., van Ginneken, B., & de Rooij, M. (2021) · European Radiology · doi:10.1007/s00330-021-07892-z · Cited by 461

Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study

Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043378 · Cited by 351

Will ChatGPT transform healthcare?

(2023) · Nature Medicine · doi:10.1038/s41591-023-02289-5 · Cited by 214

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

van Doorn, W. P. T. M., Stassen, P. M., Borggreve, H. F., Schalkwijk, M. J., Stoffers, J., Bekers, O., & Meex, S. J. R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245157 · Cited by 99

Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing

Kurstjens, S., van der Horst, A., Herpers, R., Geerits, M. W. L., Kluiters, Y., Göttgens, E., Blaauw, M. J. T., Thelen, M., Elisen, M. G. L. M., & Kusters, R. (2020) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2020-0593 · Cited by 69

A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians

Takita, H., Kabata, D., Walston, S. L., Tatekawa, H., Saito, K., Tsujimoto, Y., Miki, Y., & Ueda, D. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01543-z · Cited by 203

Human intelligence versus Chat-GPT: who performs better in correctly classifying patients in triage?

Zaboli, A., Brigo, F., Sibilio, S., Mian, M., & Turcato, G. (2024) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2024.02.008 · Cited by 85

Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians

Barak-Corren, Y., Wolf, R., Rozenblum, R., Creedon, J. K., Lipsett, S. C., Lyons, T. W., Michelson, K. A., Miller, K. A., Shapiro, D. J., Reis, B. Y., et al. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.039 · Cited by 75

Application of large language models in disease diagnosis and treatment

Yang, X., Li, T., Su, Q., Liu, Y., Kang, C., Lyu, Y., Zhao, L., Nie, Y., & Pan, Y. (2024) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003456 · Cited by 76

Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study

Ke, Y., Yang, R., Lie, S. A., Lim, T. X. Y., Ning, Y., Li, I., Abdullah, H. R., Ting, D. S. W., & Liu, N. (2024) · Journal of Medical Internet Research · doi:10.2196/59439 · Cited by 64

Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis

Wang, L., Li, J., Zhuang, B., Huang, S., Fang, M., Wang, C., Li, W., Zhang, M., & Gong, S. (2025) · Journal of Medical Internet Research · doi:10.2196/64486 · Cited by 51

The AI Future of Emergency Medicine

Petrella, R. J. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.031 · Cited by 43

The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians

Scott, I. A., & Zuccon, G. (2024) · Internal Medicine Journal · doi:10.1111/imj.16393 · Cited by 36

Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study

Peng, Z., Ma, R., Zhang, Y., Yan, M., Lu, J. L., Cheng, Q., Liao, J., Zhang, Y., Wang, J., Zhao, Y., et al. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1323924 · Cited by 29

Generative artificial intelligence: implications for biomedical and health professions education

Hersh, W. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094756 · Cited by 28

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries

Pressman, S. M., Borna, S., Gomez-Cabello, C. A., Haider, S. A., & Forte, A. J. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13102832 · Cited by 27

Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice

Madaudo, C., Parlati, A. L. M., Lisi, D. D., Carluccio, R., Sucato, V., Vadalà, G., Nardi, E., Macaione, F., Cannata, A. N., Manzullo, N., et al. (2024) · Journal of Cardiovascular Medicine · doi:10.2459/jcm.0000000000001664 · Cited by 28

Testing and Evaluation of Generative Large Language Models in Electronic Health Record Applications: A Systematic ReviewPreprint

Du, X.-S., Zhou, Z., Wang, Y.-F., Chuang, Y.-W., Li, Y.-M., Yang, R., Zhang, W., Wang, X., Chen, X.-Y., Guan, H., et al. (2024) · medRxiv · doi:10.1101/2024.08.11.24311828 · Cited by 36

The Comparative Diagnostic Capability of Large Language Models in Otolaryngology

Warrier, A. K., Singh, R., Haleem, A., Zaki, H., & Eloy, J. A. (2024) · The Laryngoscope · doi:10.1002/lary.31434 · Cited by 28

Leveraging open-source large language models for clinical information extraction in resource-constrained settings

Builtjes, L., Bosma, J. S., Prokop, M., van Ginneken, B., & Hering, A. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf109 · Cited by 30

Prediction of outcomes after cardiac arrest by a generative artificial intelligence model

Amacher, S. A., Arpagaus, A., Sahmer, C., Becker, C., Groß, S., Urben, T., Tisljar, K., Sutter, R., Marsch, S. C. U., & Hunziker, S. (2024) · Resuscitation Plus · doi:10.1016/j.resplu.2024.100587 · Cited by 26

Performance of ChatGPT and Dental Students on Concepts of Periodontal Surgery

Li, C., Zhang, J., Abdul‐Masih, J., Zhang, S., & Yang, J. (2024) · European Journal Of Dental Education · doi:10.1111/eje.13047 · Cited by 23

Preliminary analysis of the impact of lab results on large language model generated differential diagnoses

Bhasuran, B., Jin, Q., Xie, Y., Yang, C., Hanna, K., Costa, J., Shavor, C., Han, W., Lu, Z., & He, Z. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01556-8 · Cited by 26

Clinical Performance and Communication Skills of ChatGPT Versus Physicians in Emergency Medicine: Simulated Patient Study

Park, C., An, M. H., Hwang, G., Park, R. W., & An, J. (2025) · JMIR Medical Informatics · doi:10.2196/68409 · Cited by 22

Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study

Yu, S., Sun, W., Mi, D., Jin, S., Wu, X. F., Xin, B., Zhang, H., Wang, Y., Sun, X., & He, X. J. (2024) · Bioengineering · doi:10.3390/bioengineering11111159 · Cited by 19

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department

Shah-Mohammadi, F., & Finkelstein, J. (2024) · Diagnostics · doi:10.3390/diagnostics14161779 · Cited by 23

In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review

Jiménez-Sánchez, A., Avlona, N.-R., de Boer, S., Campello, V. M., Feragen, A., Ferrante, E., Ganz, M., Gichoya, J. W., González, C., Groefsema, S., et al. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732035 · Cited by 27

Performance evaluation of ChatGPT in detecting diagnostic errors and their contributing factors: an analysis of 545 case reports of diagnostic errors

Harada, Y., Suzuki, T., Harada, T., Sakamoto, T., Ishizuka, K., Miyagami, T., Kawamura, R., Kunitomo, K., Nagano, H., Shimizu, T., et al. (2024) · BMJ Open Quality · doi:10.1136/bmjoq-2023-002654 · Cited by 18

Skin and Digital–The 2024 Narrative

du Crest, D., Madhumita, M., Enbiale, W., Zink, A., Papier, A., Matewa, G. T., Castro, H., Perandones‐González, H., Guzman, J. D., Rosenbach, M. A., et al. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.05.008 · Cited by 17

Venous thromboembolism in the era of machine learning and artificial intelligence in medicine

Gil, M. R., Pantanowitz, J., & Rashidi, H. H. (2024) · Thrombosis Research · doi:10.1016/j.thromres.2024.109121 · Cited by 17

Large language models in pathology: A comparative study of ChatGPT and Bard with pathology trainees on multiple-choice questions

Du, W., Jin, X., Harris, J. C., Brunetti, A., Johnson, E., Leung, O. J., Li, X., Walle, S., Yu, Q., Zhou, X. L., et al. (2024) · Annals of Diagnostic Pathology · doi:10.1016/j.anndiagpath.2024.152392 · Cited by 16

Can ChatGPT-4 evaluate whether a differential diagnosis list contains the correct diagnosis as accurately as a physician?

Mizuta, K., Hirosawa, T., Harada, Y., & Shimizu, T. (2024) · Diagnosis · doi:10.1515/dx-2024-0027 · Cited by 14

Comparison of Two Symptom Checkers (Ada and Symptoma) in the Emergency Department: Randomized, Crossover, Head-to-Head, Double-Blinded Study

Knitza, J., Hasanaj, R., Beyer, J., Ganzer, F., Slagman, A. C., Bolanaki, M., Napierala, H., Schmieding, M. L., Al-Zaher, N., Orlemann, T., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/56514 · Cited by 12

Large language model application in emergency medicine and critical care

Hwai, H., Ho, Y.-J., Wang, C., & Huang, C. (2024) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2024.08.032 · Cited by 13

Using Large Language Models to Extract Core Injury Information From Emergency Department Notes

Choi, D. H., Kim, Y., Choi, S. W., Kim, K. H., Choi, Y. H., & Shin, S. D. (2024) · Journal of Korean Medical Science · doi:10.3346/jkms.2024.39.e291 · Cited by 12

AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information

Ezanno, A.-C., Fougerousse, A. C., Pruvost‐Balland, C., Maccari, F., & Fite, C. (2024) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s478309 · Cited by 10

AI and Big Data: Current and Future Nursing Practitioners’ Views on Future of Healthcare Education Provision

Bonacaro, A., Rubbi, I., Artioli, G., Monaco, F., Sarli, L., & Guasconi, M. (2024) · Studies in health technology and informatics · doi:10.3233/shti240134 · Cited by 15

The role of artificial intelligence in gynecologic and obstetric emergencies

Psilopatis, I., Heindl, F., Cupisti, S., Fischer, U., Kohlmann, V., Schneider, M. A., Bader, S., Krückel, A., & Emons, J. (2025) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2025.01.007 · Cited by 9

Preliminary evaluation of ChatGPT model iterations in emergency department diagnostics

Wang, J., Shue, K., Liu, L., & Hu, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-95233-1 · Cited by 11

Artificial Intelligence and Machine Learning—Powerful Yet Underutilized Tools and Algorithms in Physical Activity and Sedentary Behavior Research

Farrahi, V., & Clare, P. J. (2024) · Journal of Physical Activity and Health · doi:10.1123/jpah.2024-0021 · Cited by 10

Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?

Marshall, C. R., Forbes, J., Seidman, M. D., Roldan, L. P., & Atkins, J. (2024) · Otology & Neurotology · doi:10.1097/mao.0000000000004267 · Cited by 9

The potential, limitations, and future of diagnostics enhanced by generative artificial intelligence

Hirosawa, T., & Shimizu, T. (2024) · Diagnosis · doi:10.1515/dx-2024-0095 · Cited by 7

Evaluating the Utility of ChatGPT in Diagnosing and Managing Maxillofacial Trauma

Rothchild, E., Baker, C., Smith, I. T., Tanna, N., & Ricci, J. A. (2024) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000010931 · Cited by 7

A large language model based pipeline for extracting information from patient complaint and anamnesis in clinical notes for severity assessment

Gao, H., Wang, K., Yuan, Y., Wang, Y., Liu, Q., Wang, Y., Sun, J. Q., Wang, W., Wang, H., Zhou, S., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-07649-4 · Cited by 9

AI Based Clinical Decision-Making Tool for Neurologists in the Emergency Department

Gorenshtein, A., Fistel, S., Sorka, M., Telman, G., Winer, R., Peretz, S., Aran, D., & Shelly, S. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14176333 · Cited by 8

Identifying Bias at Scale in Clinical Notes Using Large Language Models

Apakama, D. U., Nguyen, K.-A.-N., Hyppolite, D., Soffer, S., Mudrik, A., Ling, E. J., Moses, A., Temnycky, I., Glasser, A., Anderson, R., et al. (2025) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2025.100296 · Cited by 14

Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports

Hirosawa, T., Harada, Y., Tokumasu, K., Shiraishi, T., Suzuki, T., & Shimizu, T. (2024) · JMIR Formative Research · doi:10.2196/64844 · Cited by 7

Using conversant artificial intelligence to improve diagnostic reasoning: ready for prime time?

Scott, I. A., Miller, T., & Crock, C. (2024) · The Medical Journal of Australia · doi:10.5694/mja2.52401 · Cited by 6

Faster and better than a physician?: Assessing diagnostic proficiency of ChatGPT in misdiagnosed individuals with neuromyelitis optica spectrum disorder

Shan, K., Patel, M. A., McCreary, M., Punnen, T. G., Villalobos, F., Tardo, L. M., Horton, L. A., Sguigna, P. V., Blackburn, K. M., Munoz, S. B., et al. (2024) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2024.123360 · Cited by 6

A Practical Guide to the Utilization of ChatGPT in the Emergency Department: A Systematic Review of Current Applications, Future Directions, and Limitations

Meyer, N. S., & Meyer, J. W. (2025) · Cureus · doi:10.7759/cureus.81802 · Cited by 7

Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice

Kopka, M., He, L., & Feufel, M. A. (2026) · Communications Medicine · doi:10.1038/s43856-026-01466-0 · Cited by 5

Identifying and Characterizing Bias at Scale in Clinical Notes Using Large Language ModelsPreprint

Apakama, D. U., Nguyen, K.-A.-N., Hyppolite, D., Soffer, S., Mudrik, A., Ling, E. J., Moses, A., Temnycky, I., Glasser, A., Anderson, R., et al. (2024) · medRxiv · doi:10.1101/2024.10.24.24316073 · Cited by 5

Evaluating multimodal ChatGPT for emergency decision-making of ocular trauma cases

Xue, J., Wang, Z., Chen, N., Wu, Y., Shen, Z., Shao, Y., Zhou, H., & Li, Z. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1564054 · Cited by 5

The Potential and Pitfalls of ChatGPT in Toxicological Emergencies

Kuas, Ç., Çanakçı, M. E., Acar, N., Kanbakan, A., Çetin, M., & Günsoy, E. (2025) · Journal of Emergency Medicine · doi:10.1016/j.jemermed.2025.07.002 · Cited by 4

Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis

Sanduleanu, S., Ersahin, K., Bremm, J., Talibova, N., Damer, T., Erdogan, M., Kottlors, J., Goertz, L., Bruns, C. J., Maintz, D. C., et al. (2024) · AI · doi:10.3390/ai5040096 · Cited by 5

Knowledge Distillation and Transformer-Based Framework for Automatic Spine CT Report Generation

Batool, H., Mukhtar, A., Khawaja, S. G., Alghamdi, N. S., Khan, A. M., Qayyum, A., Adil, R., Khan, Z., Akram, M. U., Akbar, M. U., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3546131 · Cited by 5

医学数字人GPT的研究现状及展望

韦, 球., 蒋, 维., 杨, 超., & 白, 春. (2024) · 元宇宙医学. · doi:10.61189/059703zeipzv · Cited by 4

Replicating Reason: The Advent of Human-like Audit Judgment by Generative AIPreprint

Isack, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4716860 · Cited by 7

Clinical validity of the Pediatric Assessment Triangle in a pediatric emergency department

Zhu, S., Wu, Y., Yu, B., Shen, B., Fang, L., Li, B., & Xiang, L. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1435604 · Cited by 3

A scoping review: how evaluation methods shape our understanding of ChatGPT’s effectiveness in healthcare

Liu, Y., ZHANG, Y., & Mao, H. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106248 · Cited by 2

Apomediation, AI, and the illusion of autonomy: risks of misinformation in patient decision-making

Rojo, A. P., Fontaines-Ruíz, T., & Romero-Rodríguez, L. M. (2026) · Frontiers in Communication · doi:10.3389/fcomm.2025.1684370 · Cited by 3

Application of artificial intelligence for ophthalmic emergencies in emergency departments: A narrative review

Chan, V. T. T., & Chan, N. C. Y. (2026) · AJO International · doi:10.1016/j.ajoint.2026.100225 · Cited by 2

The double-edged nature of ChatGPT in self-diagnosis

Koga, S. (2024) · Wiener klinische Wochenschrift · doi:10.1007/s00508-024-02343-3 · Cited by 1

Aplicación de chatbots de inteligencia artificial generativa en el campo de la anestesia

Barroso, A., & Casans, R. (2024) · Revista Española de Anestesiología y Reanimación · doi:10.1016/j.redar.2024.501667 · Cited by 1

Advancing emergency upper extremity care: A pilot study of ChatGPT's potential role in diagnosing and managing hand and wrist trauma

Rothchild, E., Jung, G., Aiello, C., Tanna, N., & Ricci, J. A. (2025) · Journal of Hand and Microsurgery · doi:10.1016/j.jham.2025.100260 · Cited by 1

ChatGPT as a clinical support tool: A comprehensive review of applications, assessment, and implementation challenges

Liu, Y., & Zhang, Y. (2025) · Physiotherapy Practice and Research · doi:10.1177/22130683251379638 · Cited by 1

Comparative feasibility of reasoning and non-reasoning large language models for gynecologic cancer emergency care

Kim, J., Kim, U., Bae, J. K., Kim, M., Ham, J. Y., Lim, J., Kim, J. H., Seo, S., Kang, S., Park, S., et al. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13668-8 · Cited by 2

Evaluating Generative AI (Microsoft Copilot) as an Adjunctive Decision-Support System in Oral and Maxillofacial Radiology: A Retrospective Study

Jagadeesh, Y., Rizvi, N., & Nair, M. K. (2026) · Oral · doi:10.3390/oral6010010 · Cited by 1

Supporting postgraduate exam preparation with large language models: implications for traditional Chinese medicine education

Wang, B., Zhang, M., Wang, Z., Yao, K., Hao, M., Wang, J., Peng, S., & Zhu, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2025.1667104 · Cited by 1

The organization as an intelligent entity: Using artificial intelligence to explore the sustainability of family and non-family firms

Riefolo, M., Grosskopf, S., & Ahrens, J. (2026) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2025.100700 · Cited by 1

Large language models and eye education: A narrative review

Andrew, A., & Rodenko, S. (2026) · Oman Journal of Ophthalmology · doi:10.4103/ojo.ojo_223_24 · Cited by 1

A Pilot Study to Evaluate Artificial Intelligence-Driven Early Retrieval of Medical Histories in the Emergency Department

Morley-Fletcher, A., Raghavan, V. R., Geanacopoulos, A. T., Maher, M., Waltzman, M. L., Barak‐Corren, Y., & Fine, A. M. (2026) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2026.01.022 · Cited by 1

ChatGPT-4 in comparison with traumatological junior doctors in emergency room cases at a level 1 trauma centre – a pilot study

Wegmann, S., Leyendecker, J., Lenz, M., Sarter, M., Müller, L. P., & Weber, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01276-2 · Cited by 1

Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges

Livieratos, A., Lin, J., Chasani, P., Gaga, M., Fousekis, F. S., Gogos, C. A., Akinosoglou, K., Katsanos, K. H., & Gamalo, M. (2026) · Methods and Protocols · doi:10.3390/mps9030069 · Cited by 1

Diagnostic Dilemma: ChatGPT Can’t Tell You What You Don’t Already Know

Preiksaitis, C. M., & Rose, C. C. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2023.09.024 · Cited by 1

Key Insight Into Enhancing Medical Image Processing Through ChatGPT: Future Direction and Applications

Verma, K., Srivastava, S., & Mishra, R. K. (2024) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/globalaisummit62156.2024.10947832 · Cited by 1

Performance of GPT-4 and mainstream Chinese Large Language Models on the Chinese Postgraduate Examination dataset: Potential for AI-assisted Traditional Chinese MedicinePreprint

Peng, S., Zhu, Y. L., Wang, B., Zhang, M., Wang, Z., Yao, K., Hao, M., & Wang, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4392855/v1 · Cited by 1

ChatGPT Is Still Not Good Enough at Giving Care-Seeking Advice, or Is It?Preprint

Kopka, M., He, L., & Feufel, M. A. (2025) · medRxiv · doi:10.1101/2025.05.13.25327519 · Cited by 1

LabQAR: A Manually Curated Dataset for Question Answering on Laboratory Test Reference Ranges and InterpretationPreprint

Bhasuran, B., Jin, Q., Deville, A., Wu, Y., Hanna, K., Lu, Z., & He, Z. (2025) · medRxiv · doi:10.1101/2025.06.03.25328882 · Cited by 1

3Ps for ChatGPT: Best Practices for Generative AI Discharge Instructions (Preprint)Preprint

Kreisman, A., Matonis, D., Wray, A., Toohey, S., & Wiechmann, W. F. (2024) · doi:10.2196/preprints.56256

In reply

ten Berg, H., van Bakel, B. M. A., van de Wouw, L., Jie, K. E., Schipper, A., Jansen, H., O’Connor, R. D., van Ginneken, B., & Kurstjens, S. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2023.10.007

Ärztliches Denken – ärztliche Denkfehler

Janneck, M. (2024) · Rheuma Plus / Schweiz · doi:10.1007/s44332-024-00022-z

Accuracy of Different Generative Artificial Intelligence Models in Medical Question Answering: A Systematic Review and Network Meta-AnalysisPreprint

Wang, L., Li, J., Zhuang, B., Huang, S., Wang, C., Li, W., Fang, M., & Gong, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4888108

MGPT 临床应用的真实世界研究设计方案

杨, 达., 宣, 建., 蒋, 维., & 白, 春. (2024) · 元宇宙医学. · doi:10.61189/010512hlgveq

Performance of GPT-4 and mainstream Chinese Large Language Models on the Chinese Postgraduate Examination dataset: Potential for AI-assisted Traditional Chinese Medicine (Preprint)Preprint

Peng, S., Zhu, Y. L., Wang, B., Zhang, M., Wang, Z., Yao, K., Hao, M., & Wang, J. (2024) · doi:10.2196/preprints.68152

Evaluación cualitativa de modelos de inteligencia artificial generativa para resolución de preguntas clínicas de rehabilitación infantil

Palomer, D. G., & SPOERER, M. A. (2025) · Rehabilitación Integral · doi:10.51230/ri.v18i1.98 · Cited by 1

Application of generative artificial intelligence chatbots in the field of anesthesia

Barroso, A., & Casans, R. (2025) · Revista Española de Anestesiología y Reanimación (English Edition) · doi:10.1016/j.redare.2025.501667

Large Language Models-Assisted Diagnosis of Catecholaminergic Polymorphic Ventricular Tachycardia in a Pediatric Cardiac Arrest PatientPreprint

Liao, X., Lei, C., Zheng, X., Wu, W. Q., Xu, X., Zhang, Z., Zhou, G., & Qu, X. (2025) · Research Square · doi:10.21203/rs.3.rs-6384159/v1

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

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