Large language model AI chatbots require approval as medical devices
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Healthcare is a uniquely demanding domain for XAI, where incorrect or biased decisions can directly impact patient outcomes and regulatory frameworks increasingly mandate algorithmic transparency.
“Healthcare represents a uniquely demanding domain for explainable AI (XAI), where incorrect or biased decisions can directly impact patient outcomes and where regulatory frameworks increasingly mandate algorithmic transparency [7].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [7]
The integration of foundation models in operable clinical software systems faces legal and regulatory challenges because these models require approval as medical devices.
“Thirdly, the integration of such models in operable clinical software systems faces legal and regulatory challenges because these models require approval as medical devices26.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [26]
Regulation of large language models is evolving and debated, including which contexts and intended uses qualify them as medical devices.
“A particularly evolving and debated topic is the regulation of LLMs, and in which contexts and intended uses do they qualify as medical devices (191,192).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [192]
The need for human oversight and validation is accentuated by the lack of professional guidelines or regulatory oversight within this field.
“This need is accentuated by the lack of professional guidelines or regulatory oversight within this field23.”
From The ethics of ChatGPT in medicine and healthcare: a systematic review on large language models (LLMs) (2024) · cites this paper as [23]
Cites
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083 · see citing works
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
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 · see citing works
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 · see citing works
What ChatGPT and generative AI mean for science
Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828 · see citing works
Korngiebel, D. M., & Mooney, S. D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00464-x · Cited by 259 · see citing works
Van Riel, N., Auwerx, K., Debbaut, P., Van Hees, S., & Schoenmakers, B. (2017) · BJGP Open · doi:10.3399/bjgpopen17x100833 · Cited by 159 · see citing works
AI chatbots are coming to search engines — can you trust the results?
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00423-4 · Cited by 23 · see citing works
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The future landscape of large language models in medicine
Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J.-N., Laleh, N. G., Löffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00370-1 · Cited by 1,045 · see citing works
The application of large language models in medicine: A scoping review
Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109713 · Cited by 414 · see citing works
Haltaufderheide, J., & Ranisch, R. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01157-x · Cited by 396 · see citing works
Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 309 · see citing works
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Qiu, J., Li, L., Sun, J., Peng, J., Shi, P., Zhang, R., Dong, Y., Lam, K., Lo, F. P.-W., Xiao, B. J., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3316750 · Cited by 246 · see citing works
D’Antonoli, T. A., Stanzione, A., Blüthgen, C., Vernuccio, F., Ugga, L., Klontzas, M. E., Cuocolo, R., Cannella, R., & Koçak, B. (2023) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2023.232417 · Cited by 232 · see citing works
The Opportunities and Risks of Large Language Models in Mental Health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J., & Jones Bell, M. (2024) · JMIR Mental Health · doi:10.2196/59479 · Cited by 235 · see citing works
A guide to artificial intelligence for cancer researchers
Pérez-López, R., Laleh, N. G., Mahmood, F., & Kather, J. N. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00694-7 · Cited by 220 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Ferber, D., El Nahhas, O. S. M., Wölflein, G., Wiest, I. C., Clusmann, J., Leßmann, M.-E., Foersch, S., Lammert, J., Tschochohei, M., Jäger, D., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-00991-6 · Cited by 175 · see citing works
In-context learning enables multimodal large language models to classify cancer pathology images
Ferber, D., Wölflein, G., Wiest, I. C., Ligero, M., Sainath, S., Laleh, N. G., Nahhas, O. S. M. E., Müller‐Franzes, G. A., Jäger, D., Truhn, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51465-9 · Cited by 167 · see citing works
Freyer, O., Wiest, I. C., Kather, J. N., & Gilbert, S. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00124-9 · Cited by 162 · see citing works
Self-citationFraser, H., Crossland, D., Bacher, I., Ranney, M., Madsen, T., & Hilliard, R. (2023) · JMIR mHealth and uHealth · doi:10.2196/49995 · Cited by 128 · see citing works
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Reis, M., Reis, F., & Kunde, W. (2024) · Nature Medicine · doi:10.1038/s41591-024-03180-7 · Cited by 130 · see citing works
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Templin, T., Perez, M. W., Sylvia, S., Leek, J. T., & Sinnott-Armstrong, N. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000503 · Cited by 115 · see citing works
Huang, Y., Gomaa, A., Semrau, S., Haderlein, M., Lettmaier, S., Weißmann, T., Grigo, J., Tkhayat, H. B., Frey, B., Gaipl, U. S., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1265024 · Cited by 116 · see citing works
A comprehensive review of large language models: issues and solutions in learning environments
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Large Language Models lack essential metacognition for reliable medical reasoning
Griot, M., Hemptinne, C., Vanderdonckt, J., & Yuksel, D. (2025) · Nature Communications · doi:10.1038/s41467-024-55628-6 · Cited by 113 · see citing works
“For instance, studies examining speci fi c tasks, such as ICD (International Classi fi cation of Diseases) coding 9 , have revealed signi fi cant performance de fi ciencies, under-scoring the need for more comprehensive evaluation frameworks 10,11 .”
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Nordblom, N. F., Büttner, M., & Schwendicke, F. (2024) · Journal of dentistry research · doi:10.1177/00220345241235606 · Cited by 118 · see citing works
Clinical and Surgical Applications of Large Language Models: A Systematic Review
Pressman, S. M., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Haider, C. R., & Forte, A. J. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13113041 · Cited by 107 · see citing works
Artificial intelligence in neurology: opportunities, challenges, and policy implications
Voigtlaender, S., Pawelczyk, J., Geiger, M., Vaios, E., Karschnia, P., Cudkowicz, M., Dietrich, J., Haraldsen, I., Feigin, V. L., Owolabi, M. O., et al. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12220-8 · Cited by 106 · see citing works
BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study
Cozzi, A., Pinker, K., Hidber, A., Zhang, T., Bonomo, L., Gullo, R. L., Christianson, B., Curti, M., Rizzo, S., Grande, F. D., et al. (2024) · Radiology · doi:10.1148/radiol.232133 · Cited by 96 · see citing works
Derraz, B., Breda, G., Kaempf, C., Baenke, F., Cotte, F., Reiche, K., Köhl, U., Kather, J. N., Eskenazy, D., & Gilbert, S. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00517-w · Cited by 94 · see citing works
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Cui, J., Ning, M., Li, Z., Li, H., Yang, Y., Chen, B., Ling, B., Tian, Y., & Yuan, L. (2026) · Fundamental Research · doi:10.1016/j.fmre.2026.03.026 · Cited by 309 · see citing works
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Self-citationChow, J. C. L., & Li, K. (2024) · JMIR Bioinformatics and Biotechnology · doi:10.2196/64406 · Cited by 75 · see citing works
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Artificial intelligence in liver cancer — new tools for research and patient management
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He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2024) · doi:10.2139/ssrn.4809363 · Cited by 69 · see citing works
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Büttner, M., Leser, U., Schneider, L., & Schwendicke, F. (2023) · Journal of Dentistry · doi:10.1016/j.jdent.2023.104796 · Cited by 65 · see citing works
Guizani, S., Mazhar, T., Shahzad, T., Ahmad, W., Bibi, A., & Hamam, H. (2025) · Discover Education · doi:10.1007/s44217-025-00424-7 · Cited by 62 · see citing works
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Wu, K., Wu, E., Wei, K., Zhang, A., Casasola, A., Nguyen, T., Riantawan, S., Shi, P., Ho, D., & Zou, J. (2025) · Nature Communications · doi:10.1038/s41467-025-58551-6 · Cited by 72 · see citing works
“Although no commercially available LLMs are currently approved by the FDA for use in medical decision support settings 1 , top-performing LLMs like GPT-4o, Claude, and Med-PaLM have nonetheless demonstrated superior performance over clinicians on medical exams like the US Medical Licensing Exam…”
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Ghim, J., & Ahn, S. (2023) · Translational and Clinical Pharmacology · doi:10.12793/tcp.2023.31.e16 · Cited by 61 · see citing works
O'Reilly, D., McGrath, J., & Martin-Loeches, I. (2024) · Journal of Intensive Medicine · doi:10.1016/j.jointm.2023.10.001 · Cited by 55 · see citing works
Habicht, J., Dina, L.-M., McFadyen, J., Stylianou, M., Harper, R., Hauser, T. U., & Rollwage, M. (2025) · Journal of Medical Internet Research · doi:10.2196/60435 · Cited by 52 · see citing works
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Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 58 · see citing works
Rollwage, M., Juechems, K., Habicht, J., Carrington, B., Hauser, T. U., & Harper, R. J. (2024) · BMJ Innovations · doi:10.1136/bmjinnov-2023-001110 · Cited by 48 · see citing works
Maitland, A., Fowkes, R., & Maitland, S. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080558 · Cited by 48 · see citing works
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Ducret, M., Wahal, E., Gruson, D., Amrani, S., Richert, R., Mouncif-Moungache, M., & Schwendicke, F. (2024) · Journal of dentistry research · doi:10.1177/00220345241271160 · Cited by 45 · see citing works
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Wekenborg, M., Gilbert, S., & Kather, J. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01559-5 · Cited by 49 · see citing works
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Shahab, O., Kurdi, B. E., Shaukat, A., Nadkarni, G., & Soroush, A. (2024) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848241227031 · Cited by 43 · see citing works
Branley-Bell, D., Brown, R. D., Coventry, L., & Sillence, E. (2023) · Frontiers in Communication · doi:10.3389/fcomm.2023.1275127 · Cited by 42 · see citing works
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Blüthgen, C., Veen, D. V., Zakka, C., Link, K. E., Fanous, A. H., Daneshjou, R., Frauenfelder, T., Langlotz, C. P., Gatidis, S., & Chaudhari, A. S. (2025) · Radiology · doi:10.1148/radiol.240528 · Cited by 41 · see citing works
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Umeton, R., Kwok, A., Maurya, R., Leco, D., Lenane, N., Willcox, J., Abel, G. A., Tolikas, M., & Johnson, J. M. (2024) · NEJM AI · doi:10.1056/aics2300191 · Cited by 37 · see citing works
Hauser-Ulrich, S., Lienhard, N., Künzli, H., & Kowatsch, T. (2024) · JMIR mhealth and uhealth · doi:10.2196/54945 · Cited by 49 · see citing works
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Castonguay, A., Wagner, G., Motulsky, A., & Paré, G. (2023) · Health Policy · doi:10.1016/j.healthpol.2023.104938 · Cited by 40 · see citing works
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Jung, K.-H. (2023) · Korean Journal of Radiology · doi:10.3348/kjr.2023.0790 · Cited by 34 · see citing works
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Liu, C., Liu, Z., Holmes, J., Zhang, L., Zhang, L., Ding, Y., Shu, P., Wu, Z., Dai, H., Li, Y., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100045 · Cited by 34 · see citing works
Gilbert, S., Adler, R., Holoyad, T., & Weicken, E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01482-9 · Cited by 33 · see citing works
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Luetke Lanfer, H., Reifegerste, D., Weber, W., Memenga, P., Baumann, E., Geulen, J., Klein, S., Müller, A., Hahne, A., & Weg-Remers, S. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-10785-8 · Cited by 30 · see citing works
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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 · see citing works
Hetz, M. J., Carl, N., Haggenmüller, S., Wies, C., Kather, J. N., Michel, M., Wessels, F., & Brinker, T. J. (2024) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100078 · Cited by 31 · see citing works
Ma, Y., Liu, T., Qi, J., Gan, Y., Cheng, Q., Xiao, M., & Wang, J. (2025) · Journal of Advanced Nursing · doi:10.1111/jan.16655 · Cited by 29 · see citing works
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Dai, Z.-Y., Wang, F., Shen, C., Ji, Y.-L., Li, Z.-Y., Wang, Y., & Pu, Q. (2025) · Journal of Medical Internet Research · doi:10.2196/67488 · Cited by 29 · see citing works
Lynch, C. J., Jensen, E. J., Zamponi, V., O’Brien, K., Frydenlund, E. F., & Gore, R. J. (2023) · Future Internet · doi:10.3390/fi15120375 · Cited by 31 · see citing works
Schneider, C., Nißen, M., Kowatsch, T., & Vinay, R. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080545 · Cited by 27 · see citing works
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Campion, J. R., O’Connor, D. B., & Lahiff, C. J. (2024) · World Journal of Gastrointestinal Endoscopy · doi:10.4253/wjge.v16.i3.126 · Cited by 27 · see citing works
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Foote, H. P., Hong, C., Anwar, M. M., Borentain, M., Bugin, K., Dreyer, N. A., Fessel, J. P., Goyal, N., Hanger, M., Hernandez, A. F., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101593 · Cited by 30 · see citing works
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Inojosa, H., Voigt, I., Wenk, J., Ferber, D., Wiest, I. C., Antweiler, D., Weicken, E., Gilbert, S., Kather, J. N., Akgün, K., et al. (2024) · Multiple Sclerosis Journal · doi:10.1177/13524585241277376 · Cited by 25 · see citing works
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Truhn, D., Azizi, S., Zou, J., Cerdá-Alberich, L., Mahmood, F., & Kather, J. N. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-025-00900-0 · Cited by 25 · see citing works
Shusterman, R., Waters, A. C., O’Neill, S. K., Bangs, M., Luu, P., & Tucker, D. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01516-2 · Cited by 22 · see citing works
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Rust, P., Frings, J., Meister, S., & Fehring, L. (2025) · Communications Medicine · doi:10.1038/s43856-025-00927-2 · Cited by 23 · see citing works
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Mohammad‐Rahimi, H., Setzer, F. C., Aminoshariae, A., Dummer, P. M. H., Duncan, H. F., & Nosrat, A. (2025) · International Endodontic Journal · doi:10.1111/iej.14231 · Cited by 22 · see citing works
Xu, B., Wu, G., Li, Z., Xu, G., Zeng, H., Tong, R., & Ng, H. Y. (2025) · npj Clean Water · doi:10.1038/s41545-025-00509-8 · Cited by 23 · see citing works
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Freyer, O., Jahed, F., Ostermann, M., Rosenzweig, C., Werner, P., & Gilbert, S. (2024) · Journal of Medical Internet Research · doi:10.2196/65528 · Cited by 21 · see citing works
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Freyer, O., Wrona, K. J., de Snoeck, Q., Hofmann, M., Melvin, T., Stratton‐Powell, A. A., Wicks, P. J., Parks, A. C., & Gilbert, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-71808-2 · Cited by 21 · see citing works
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Tang, A., Tung, N., Nguyen, H. Q., Kwok, K. O., Luong, S., Bui, N., Nguyen, G., & Tam, W. (2024) · BMJ · doi:10.1136/bmj-2024-080208 · Cited by 21 · see citing works
Ranisch, R., & Haltaufderheide, J. (2025) · Digital Society · doi:10.1007/s44206-025-00161-2 · Cited by 17 · see citing works
Foundation models for radiology—the position of the AI for Health Imaging (AI4HI) network
de Almeida, J. G., Alberich, L. C., Tsakou, G., Kostas Marias, A., Tsiknakis, M., Lekadir, K., Martí‐Bonmatí, L., & Papanikolaou, N. K. (2025) · Insights into Imaging · doi:10.1186/s13244-025-02056-9 · Cited by 21 · see citing works
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Hölscher, D. L., Bülow, R. D., Strauch, M., & Boor, P. (2025) · Kidney International · doi:10.1016/j.kint.2025.06.029 · Cited by 18 · see citing works
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Yiu, A. J., & Lam, K. (2023) · Annals of The Royal College of Surgeons of England · doi:10.1308/rcsann.2023.0085 · Cited by 18 · see citing works
Eckrich, J., Ellinger, J., Cox, A., Stein, J., Ritter, M., Blaikie, A. J., Kühn, S., & Buhr, C. R. (2024) · BJUI Compass · doi:10.1002/bco2.359 · Cited by 18 · see citing works
Kambhamettu, H., Metaxa, D., Johnson, K. B., & Head, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642573 · Cited by 18 · see citing works
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Karlsen, T. H., Hutchinson, S. J., Zelber‐Sagi, S., Carrieri, P., Negro, F., Lim, A. G., Cecchini, M., Dirac, M. A., Murray, F., Engebretsen, E., et al. (2026) · The Lancet · doi:10.1016/s0140-6736(26)00138-8 · Cited by 17 · see citing works
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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 · see citing works
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Diessen, E. G. A. L. V., van Amerongen, R. A., Zijlmans, M., & Otte, W. M. (2024) · Epilepsia · doi:10.1111/epi.17907 · Cited by 19 · see citing works
Efficient O-type mapping and routing of large-scale neural networks to torus-based ONoCs
Yao, Q., Meng, D., Yang, H., Feng, N., & Zhang, J. (2024) · Journal of Optical Communications and Networking · doi:10.1364/jocn.525666 · Cited by 16 · see citing works
Godwin, R. C., Bryant, A. S., Wagener, B. M., Ness, T. J., DeBerry, J. J., Horn, L. L., Graves, S. H., Archer, A. C., & Melvin, R. L. (2024) · SoftwareX · doi:10.1016/j.softx.2023.101601 · Cited by 15 · see citing works
Saleshat: A LLM-Based Social Robot for Human-Like Sales Conversations
Hanschmann, L., Gnewuch, U., & Maedche, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-54975-5_4 · Cited by 17 · see citing works
Incidental Prompt Injections on Vision–Language Models in Real-Life Histopathology
Clusmann, J., Schulz, S., Ferber, D., Wiest, I. C., Fernandez, A., Eckstein, M., Lange, F., Reitsam, N. G., Kellers, F., Schmitt, M. P., et al. (2025) · NEJM AI · doi:10.1056/aics2500078 · Cited by 16 · see citing works
Schläpfer, S., Schneider, F., Santhanam, P., Eicher, M., Kowatsch, T., Witt, C. M., & Barth, J. (2024) · JMIR Cancer · doi:10.2196/52386 · Cited by 14 · see citing works
Rollwage, M., McFadyen, J., Juechems, K., Balogh, A., Pisupati, S., Mircea, M.-T., Hauser, T. U., Prichard, G., & Harper, R. J. (2026) · Nature Medicine · doi:10.1038/s41591-026-04278-w · Cited by 17 · see citing works
Zaimah, N. R., Hartanto, E. B., & Zahro, F. (2023) · Mantiqu Tayr Journal of Arabic Language · doi:10.25217/mantiqutayr.v4i1.3951 · Cited by 13 · see citing works
Freyer, O., Wiest, I. C., & Gilbert, S. (2025) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2025.100196 · Cited by 12 · see citing works
Self-citationChoo, S., Yoo, S., Endo, K., Truong, B., & Son, M. H. (2025) · JMIR Nursing · doi:10.2196/63058 · Cited by 13 · see citing works
Tang, Y., Xiao, Z., Li, X., Fang, Q., Zhang, Q., Fong, D. Y. T., Lai, F. T. T., Chui, C. S. L., Chan, E. W., & Wong, I. C. K. (2024) · medRxiv · doi:10.1101/2024.03.20.24304572 · Cited by 16 · see citing works
- Authors
- Gilbert, S., Harvey, H., Melvin, T., Vollebregt, E., & Wicks, P.
- Affiliations
- Else Kröner Fresenius Center for Digital Health (DE)
- Trinity College Dublin (IE)
- Corresponding author
- Gilbert, Stephen
- Venue
- Nature Medicine
- Published
- 2023-06-30
- Volume
- 29
- Issue
- 10
- Pages
- 2396-2398
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4382678522
- Cited by
- 207
- References
- 7
- Citations by year
- 2026: 42 · 2025: 61 · 2024: 82 · 2023: 22
- FWCI
- 8.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (16KISA100K)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Healthcare Regulation
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- large language models, AI chatbots, medical device regulation, regulatory approval, patient care, AI reliability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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