Large language model as clinical decision support system augments medication safety in 16 clinical specialties
Open accessAbstract
Large language models (LLMs) have emerged as tools to support healthcare delivery, from automating tasks to aiding clinical decision-making. This study evaluated LLMs as alternative to rule-based alert systems, focusing on their ability to identify prescribing errors. This was designed as a prospective, cross-over, open-label study involving 91 error scenarios based on 40 clinical vignettes across 16 medical and surgical specialties. We developed and validated five LLM models using a retrieval-augmented generation framework. The best-performing model evaluated three different implementation strategies: LLM-based clinical decision support system (CDSS) alone, pharmacist plus LLM-based CDSS (co-pilot), and pharmacist alone. The co-pilot arm demonstrated the best performance with an accuracy of 61% (precision 0.57, recall 0.61, and F1 0.59). In detecting errors posing serious harm, the co-pilot mode increased accuracy by 1.5-fold over the pharmacist alone. Effective LLM integration for complex tasks like medication chart reviews can enhance healthcare professional performance, improving patient safety.
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These AI technologies have improved diagnostic accuracy, medication safety, workflow efficiency, and access to specialist care.
“They have improved diagnostic accuracy, medication safety, workflow efficiency, and access to specialist care 5,6 .”
From AI-induced never-skilling in medical education (2026) · cites this paper as [5]
The Results (Comparative performance of different modes of delivery; page unavailable) report better identification of drug-related problems with pharmacist–LLM collaboration, supporting the medication-safety part of the claim, although the overall accuracy difference was not statistically significant. The paper does not establish improved diagnostic accuracy, workflow efficiency, or access to specialist care; its Discussion instead describes possible workflow disruption and envisages, rather than demonstrates, filling service gaps in low-resource settings. (LLM, conf=0.88)
Experimental — accuracy not yet measuredStudies highlighted the potential risks of data privacy breaches associated with the use of proprietary LLMs in clinical practice.
“Studies highlighted the potential risks of data privacy breaches associated with the use of proprietary LLMs in clinical practice.74,79,83”
From Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations (2026) · cites this paper as [74]
The STAR★Methods section says no identifiable patient data were used, and the Discussion addresses limitations such as bias, prompting, and generalizability. Neither passage discusses a risk of privacy breaches from proprietary LLMs in clinical practice. Searches for privacy, proprietary models, and patient-data breaches found no passage addressing that claim. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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1 retracted
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040 · see citing works
RetractedLarge language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Self-citationAn overview of clinical decision support systems: Benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · see citing works
Adapted large language models can outperform medical experts in clinical text summarization
Van Veen, D., Van Uden, C., Blankemeier, L., Delbrouck, J.-B., Aali, A., Bluethgen, C., Pareek, A., Polacin, M., Reis, E. P., Seehofnerová, A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02855-5 · Cited by 891 · see citing works
with the HITEC Investigators, Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0430-8 · Cited by 709 · see citing works
Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H. R., Díaz-Martínez, J. P., TOMLINSON, G. A., Grimshaw, J., & Shojania, K. G. (2020) · BMJ · doi:10.1136/bmj.m3216 · Cited by 472 · see citing works
Health system-scale language models are all-purpose prediction engines
Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06160-y · Cited by 460 · see citing works
Economic analysis of the prevalence and clinical and economic burden of medication error in England
Elliott, R. A., Camacho, E. M., Janković, D., Sculpher, M. J., & Faria, R. (2020) · BMJ Quality & Safety · doi:10.1136/bmjqs-2019-010206 · Cited by 446 · see citing works
Medication errors: prescribing faults and prescription errors
Velo, G., & Minuz, P. (2009) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.2009.03425.x · Cited by 322 · see citing works
Zaretsky, J., Kim, J. M., Baskharoun, S., Zhao, Y., Austrian, J., Aphinyanaphongs, Y., Gupta, R., Blecker, S. B., & Feldman, J. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.0357 · Cited by 303 · see citing works
Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making
Rao, A., Kim, J., Kamineni, M., Pang, M., Lie, W., & Succi, M. D. (2023) · doi:10.1101/2023.02.02.23285399 · Cited by 257 · see citing works
Integrated image-based deep learning and language models for primary diabetes care
Li, J., Guan, Z., Wang, J., Cheung, C. Y., Zheng, Y., Lim, L.-L., Lim, C. C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03139-8 · Cited by 250 · see citing works
Self-citationCorny, J., Rajkumar, A., Martin, O., Dode, X., Lajonchère, J.-P., Billuart, O., Bézie, Y., & Buronfosse, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa154 · Cited by 225 · see citing works
A systematic review of hospitalization resulting from medicine‐related problems in adult patients
Hamid, A. A., Ghaleb, M. A., Aljadhey, H. S., & Aslanpour, Z. (2013) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.12293 · Cited by 211 · see citing works
Heterogeneity and predictors of the effects of AI assistance on radiologists
Yu, F., Moehring, A., Banerjee, O., Salz, T., Agarwal, N., & Rajpurkar, P. (2024) · Nature Medicine · doi:10.1038/s41591-024-02850-w · Cited by 200 · see citing works
Jones, G. M., Roe, N. A., Louden, L., & Tubbs, C. R. (2017) · Hospital Pharmacy · doi:10.1177/0018578717732339 · Cited by 185 · see citing works
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.16452 · Cited by 178 · see citing works
Economic impact of medication error: a systematic review
Walsh, E. K., Hansen, C. R., Sahm, L. J., Kearney, P. M., Doherty, E., & Bradley, C. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4188 · Cited by 164 · see citing works
Spall, H. G. C. V., Wallentin, L. C., Yusuf, S., Eikelboom, J. W., Nieuwlaat, R., Yang, S., Kabali, C., Reilly, P. A., Ezekowitz, M. D., & Connolly, S. J. (2012) · Circulation · doi:10.1161/circulationaha.112.101808 · Cited by 157 · see citing works
Macaron, M. M., Segun-Omosehin, O., Matar, R., Beran, A., Nakanishi, H., Than, C. A., & Abulseoud, O. A. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.1071397 · Cited by 146 · see citing works
Suharwardy, S. H., Ramachandran, M. K., Leonard, S. A., Gunaseelan, A., Lyell, D. J., Darcy, A. M., Robinson, A. H., & Judy, A. E. (2023) · AJOG Global Reports · doi:10.1016/j.xagr.2023.100165 · Cited by 127 · see citing works
Diagnostic accuracy of a large language model in rheumatology: comparison of physician and ChatGPT-4
Krusche, M., Callhoff, J., Knitza, J., & Ruffer, N. (2023) · Rheumatology International · doi:10.1007/s00296-023-05464-6 · Cited by 116 · see citing works
Olakotan, O. O., & Mohd Yusof, M. (2021) · Health Informatics Journal · doi:10.1177/14604582211007536 · Cited by 101 · see citing works
DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients
Wang, H., Gao, C., Dantona, C., Hull, B., & Sun, J. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00989-3 · Cited by 99 · see citing works
Burnout and the challenges facing pharmacists during COVID-19: results of a national survey
Johnston, K., O’Reilly, C. L., Scholz, B., Georgousopoulou, E. N., & Mitchell, I. (2021) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-021-01268-5 · Cited by 95 · see citing works
Large language models for preventing medication direction errors in online pharmacies
Pais, C., Liu, J., Voigt, R., Gupta, V., Wade, E., & Bayati, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-02933-8 · Cited by 94 · see citing works
Metsvaht, T., Nellis, G., Varendi, H., Nunn, A. J., Graham, S. M., Rieutord, A., Storme, T., McElnay, J. C., Mulla, H., Turner, M. A., et al. (2015) · BMC Pediatrics · doi:10.1186/s12887-015-0359-y · Cited by 89 · see citing works
Rozenblum, R., Rodríguez-Monguió, R., Volk, L. A., Forsythe, K. J., Myers, S., McGurrin, M., Williams, D. H., Bates, D. W., Schiff, G. D., & Seoane-Vazquez, E. C. (2019) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/j.jcjq.2019.09.008 · Cited by 75 · see citing works
Helmons, P. J., Suijkerbuijk, B. O., Panday, P. V. N., & Kosterink, J. G. W. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu010 · Cited by 72 · see citing works
The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice
Shoja, M. M., Van de Ridder, J. M. M., & Rajput, V. (2023) · Cureus · doi:10.7759/cureus.40883 · Cited by 69 · see citing works
Gates, P., Baysari, M. T., Mumford, V., Raban, M. Z., & Westbrook, J. I. (2019) · Drug Safety · doi:10.1007/s40264-019-00823-4 · Cited by 69 · see citing works
Medical artificial intelligence for clinicians: the lost cognitive perspective
Tikhomirov, L., Semmler, C., McCradden, M. D., Searston, R. A., Ghassemi, M., & Oakden‐Rayner, L. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00095-5 · Cited by 68 · see citing works
Khalifa, M., & Zabani, I. (2016) · Studies in health technology and informatics · doi:10.3233/978-1-61499-664-4-51 · Cited by 55 · see citing works
Draft Statement on Pharmaceutical Care
(1993) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/50.1.126 · Cited by 53 · see citing works
Foot, H., Scott, I. A., Sturman, N. J., Whitty, J. A., Rixon, K., Connelly, L. B., Williams, I. D., & Freeman, C. R. (2021) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2021.07.015 · Cited by 44 · see citing works
Assessment of Pathology Domain-Specific Knowledge of ChatGPT and Comparison to Human Performance
Wang, A. Y., Lin, S., Tran, C., Homer, R., Wilsdon, D., Walsh, J. C., Goebel, E. A., Sansano, I., Sonawane, S. S., Cockenpot, V., et al. (2024) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0296-oa · Cited by 42 · see citing works
Accuracy of Vitreoretinal Disease Information From an Artificial Intelligence Chatbot
Caranfa, J. T., Bommakanti, N. K., Young, B. K., & Zhao, P. Y. (2023) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2023.3314 · Cited by 34 · see citing works
Taegtmeyer, A. B., Kullak-Ublick, G. A., Widmer, N., Falk, V., & Jetter, A. (2012) · Cardiology · doi:10.1159/000343272 · Cited by 31 · see citing works
Hines, L. E., Saverno, K. R., Warholak, T. L., Taylor, A. M., Grizzle, A. J., Murphy, J. E., & Malone, D. C. (2011) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2010.10.007 · Cited by 26 · see citing works
MedDM:LLM-executable clinical guidance tree for clinical decision-makingPreprint
Li, B., Meng, T., Shi, X., Zhai, J., & Ruan, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.02441 · Cited by 23 · see citing works
Flory, J., Ancker, J. S., Kim, S. Y. H., Kuperman, G. J., Petrov, A., & Vickers, A. J. (2024) · Diabetes Care · doi:10.2337/dc24-1067 · Cited by 18 · see citing works
Seven Failure Points When Engineering a Retrieval Augmented Generation SystemPreprint
Barnett, S., Kurniawan, S., Thudumu, S., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.05856 · Cited by 9 · see citing works
Identification of kidney-related medications using AI from self-captured pill images
Sheikh, M. S., Dreesman, B., Barreto, E. F., Thongprayoon, C., Miao, J., Suppadungsuk, S., Mao, M. A., Qureshi, F., Pham, J. H., Craici, I. M., et al. (2024) · Renal Failure · doi:10.1080/0886022x.2024.2402075 · Cited by 5 · see citing works
An Automated Strategy to Calculate Medication Regimen Complexity.
Lu, Y., Green, A. R., Quiles, R., & Taylor, C. O. (2023) · PubMed Central · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [6]Osheroff J.A. Teich J.M. Levick D. Saldana L. Velasco F. Sittig D.F. Rogers K. Jenders R. Improving Outcomes with Clinical Decision Support: An Implementer's Guide Second Edition 2023 HIMSS
Cited by
Sorted by citations
AI-induced never-skilling in medical education
Ke, Y., Jin, L., Ong, J. C. L., Thirunavukarasu, A. J., Car, J., Cheung, C. Y., Tham, Y. C., Ting, D. S. W., Ong, M. E. H., Compton, S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04438-y · Cited by 35 · see citing works
Self-citationToward a science of human–AI teaming for decision making: a complementarity framework
Gonzalez, C., Donahue, K., Goldstein, D. G., Heidari, H., Jalali, M. S., Schelble, B., Singh, A., & Woolley, A. W. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag030 · Cited by 33 · see citing works
Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority
Ong, J. C. L., Ning, Y., Liu, M., Ma, Y., Zhao, L., Singh, K., Chang, R. T., Vogel, S., Lim, J. C. W., Tan, I. S. K., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02552-2 · Cited by 7 · see citing works
Self-citationCai, M., & Liu, T. (2026) · Pharmaceuticals · doi:10.3390/ph19050696 · Cited by 7 · see citing works
The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery
Buck, L., Kohler, J., Risch, J., Incesu, R., Hügelmann, K., Weiß, M.-L., Weische, O., Schließer, P., Knobloch, H. C., Blessin, N. C., et al. (2025) · Current Oncology · doi:10.3390/curroncol32120665 · Cited by 6 · see citing works
Mugambiwa, T., & Ndlovu, B. (2026) · Journal of Applied Informatics and Computing · doi:10.30871/jaic.v10i1.11900 · Cited by 4 · see citing works
“This stepwise reasoning is more intuitive for clinician thinking than end-to-end LLMs or classical predictive models, making complex decisions accessible[29].”
Application of Gemini in Public Health Amid the Artificial Intelligence Era
Tatar, M., Farokhi, S., Foumani, A. A., Uzunlar, E., & Araz, Ö. M. (2025) · IEEE Engineering Management Review · doi:10.1109/emr.2025.3577913 · Cited by 4 · see citing works
Ekingen, E., & Üçdal, M. T. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15051730 · Cited by 3 · see citing works
Üçdal, M. T., & Ekingen, E. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03593-z · Cited by 2 · see citing works
Ethical Considerations in Personal Health Large Language Models
Liu, J., & Liu, S. (2026) · Journal of Medical Internet Research · doi:10.2196/92240 · Cited by 2 · see citing works
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 · see citing works
Yıldız, C., Gökmen, M. Y., Utlu, Ç., Karabay, E. S., Tanık, U., & Pazarcı, Ö. (2025) · Indian Journal of Orthopaedics · doi:10.1007/s43465-025-01653-6 · Cited by 1 · see citing works
Farha, R. K. A., Abuzaid, H., Alalawneh, J., Sharaf, M., Al-Ghawanmeh, R., & Qunaibi, E. A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm15010162 · Cited by 1 · see citing works
Çiçek, V., Zhao, L., Tur, Y., Öz, A., Kılıç, Ş., Durak, G., Şaylık, F., Hayıroğlu, M. İ., Çınar, T., & Bağcı, U. (2026) · Medical Sciences · doi:10.3390/medsci14010033 · Cited by 1 · see citing works
Liu, Z., Ding, Y., Chen, J., Yan, Z., Cheng, X., Zhou, W., Fu, P., & Wang, Z. (2026) · JMIR Medical Informatics · doi:10.2196/97520 · Cited by 1 · see citing works
Wang, Y., Luan, S., Shang, N., Zhang, X., & Li, Q. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1710500 · Cited by 1 · see citing works
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Saxena, T., Reddy, B. H., Bansal, H., Mudgal, I., Devendiran, R., & Puralkar, H. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11526130 · Cited by 1 · see citing works
Kim, T. Y., Oh, W.-S., & Jeong, D. (2026) · Diagnostics · doi:10.3390/diagnostics16152435 · Cited by 1 · see citing works
Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations
Yakob, R. A., Bamgboje-Ayodele, A., Collins, J. C., & Aslani, P. (2026) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2026.09.007
Dermenchyan, A., Seiden, D. S., Bhayani, M., Song, A., Venkatesan, N., Sharma, K., Pandrangi, K., & Han, M. A. (2026) · Cureus · doi:10.7759/cureus.102976
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Sharma, D., & Sharma, R. K. (2026) · Journal of Ayurveda and Integrated Medical Sciences · doi:10.21760/jaims.11.3.21
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Singh, S. K., Sharma, A., Chauhan, J., Saghir, U., Ojha, N. K., & Sharma, A. (2026) · IEEE International Conference on AI Engineering and Innovations (AIEI) · doi:10.1109/aiei69164.2026.11497467
K'Orimba, K., Mwema, E., Obat, E., & Bowers, C. P. (2026) · Research Square · doi:10.21203/rs.3.rs-9596467/v1
Gürses, K. M., Tezcan, H., Tunçez, A., Özen, Y., & Yalçın, M. U. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1808572
Chuang, K.-C., Lin, H., & Lin, H.-M. (2026) · medRxiv · doi:10.64898/2026.05.23.26353939
Yeh, S.-E., Lin, H., Lai, W., & Lin, H.-M. (2026) · Frontiers in Medicine · doi:10.64898/2026.05.28.26353938
Kara, S., & Karcıoğlu, A. A. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12279-6
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Hedberg, A. (2026) · Research Square · doi:10.21203/rs.3.rs-9708115/v1
Sturmberg, J. P., & Martin, C. M. (2026) · Cureus · doi:10.7759/cureus.111382
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Memiş, H., & Çakir, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26472-6.00101-7
Stevanović, A., & Jovanović, M. (2026) · International Scientific Conference Sinteza · doi:10.15308/sinteza-2026-27-31
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Haxaire, N., Mourad-Chehade, F., Yalaoui, A., Nader, P., Gana, Z. A., & Chehade, H. (2026) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart71257.2026.11598217
Li, N., Tang, W., Li, N., Gong, C., Wei, Q., Gong, J., Zhai, J., & Zhang, S. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1826012
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Lalwani, A., Zdon, J., Addicks, M., Marzouki, Y., Seo, D., & Chisholm, O. (2026) · Pharmaceutical Medicine · doi:10.1007/s40290-026-00623-z
Botha, P., Wyk, A. V., & Marais, J. (2026) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/mvlzkjgrlu
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Rehman, F., Shahid, M. W., Hamza, M., & Ahmed, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26472-6.00023-1
Nguyen, K. A., & Zillich, A. J. (2026) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70264
España-Lopera, J. C., Núñez‐Valdéz, E. R., & García-Díaz, V. (2026) · International Conference on Federated Learning and Intelligent Computing Systems (FLICS) · doi:10.1109/flics70075.2026.11621915
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Anitha, G., & Venkatesh, N. (2023) · International Journal of Data Science and IoT Management System · doi:10.64751/ijdim.2023.v2.n3.1233
Akıncı, G., Kürkçüoğlu, B. S., & Bayhan, B. U. (2026) · doi:10.2196/preprints.107963
Khan, M. S. H., Koohsari, M. J., Li, J., Zhao, J., Luo, Y., Yasunaga, A., Oka, K., & Kaczynski, A. T. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23081035
Łabuz, M., Szczęsny, P., & Mika-Łabuz, K. (2026) · Ethics and Information Technology · doi:10.1007/s10676-026-09917-x
Fischer, D., Meier, L., Koch, S., & Braun, T. (2026) · Archives of Pharmacy Practice · doi:10.51847/whawvgp2pm
Babu, A. M., Nehemiah, A. J., Jagadeep, V., Suji, R. A., & Nallusamy, P. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1890072
Patel, A., Khand, Y., Vallamchetla, S. K., Li, P., Tao, C., & Cheung, J. (2026) · Journal of Medical Internet Research · doi:10.2196/104092
Rezaei, H., Rabhi, F., Beheshti, A., & Behnia, A. (2026) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh72779.2026.00045
Gandhi, S., & Balas, M. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16090448
Yanagita, T., Saito, T., Tsuru, S., Umeki, R., Sawamura, N., Kurata, S., & Teranishi, F. (2026) · doi:10.2196/preprints.110741
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Costa, G., Mendes, R., Teixeira, B., & Ribeiro, L. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/f858630908
Power, D., & Power, T. (2026) · Research Square · doi:10.21203/rs.3.rs-10989591/v1
Wilson, E., Jong, F. D., & Peters, L. (2026) · Annals of Pharmacy Practice and Pharmacotherapy · doi:10.51847/xt3dqdt8kp
An intelligent OCR-LLM prescription review system for inpatient narcotic and psychotropic drugs
Ma, R., Xu, K., 张鸣明, Li, X., Ma, X., Wang, J., & Li, J. (2026) · Medicine · doi:10.1097/md.0000000000050741
Large Language Models for NiTi File‐Group Selection in Endodontics: A Two‐Stage Comparative Study
Erkal, D., Çakmak, Y. E., & Er, K. (2026) · Australian Endodontic Journal · doi:10.1111/aej.70129
Ali, A. B., Gharbi, S., & Jebali, N. (2026) · Annals of Pharmacy Practice and Pharmacotherapy · doi:10.51847/nrvbaxjhib
Mokoena, N., Novak, P., & Mendes, S. (2024) · Annals of Pharmacy Practice and Pharmacotherapy · doi:10.51847/nju0nk3jgz
Beyond the Black Box: A Point-Of-Care Framework for LLM Literacy and Critical Appraisal
Stephenson, C. R., Aakre, C. A., Croghan, I. T., Hurt, R. T., & West, C. P. (2026) · Journal of Primary Care & Community Health · doi:10.1177/21501319261491056
Eroğlu, Ö. O., & EROĞLU, C. (2026) · Journal of Medical Internet Research · doi:10.2196/98131
Salam, A. A., Chiranth, R., Benakappa, A., Shrivastava, S. K., & Rao, S. K. (2026) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.5499
Xia, Y., Yan, M., Jiang, L., Zhao, Y., Li, Y., Yao, M., Zhang, R., Zhen, J., Yang, X., Wu, L., et al. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1906806
Koliou, N., Kourtidis, L., Lykoudis, P. M., Karavolias, I., Charalampous, S., Ayiomamitis, G. D., Kouki, P., & Iordanou, C. (2026) · Healthcare · doi:10.3390/healthcare14193331
Niriella, M. A., Karunanayake, R., Dhanasekara, V., Samarasinghe, S., Chathurika, A., Senanayake, M., Kodisinghe, K. S., Jayasena, H., Abeynayake, D., Jayasundera, H., et al. (2026) · doi:10.22541/authorea.15010297/v1
An, X., Cai, J., Cao, X., & Li, J. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1980631
Large language models show product name convergence in drug recommendations
Sato, J., Walston, S. L., Takita, H., Mitsuyama, Y., Saito, K., Wu, J., & Ueda, D. (2026) · Nature Health · doi:10.1038/s44360-026-00205-y
Sunday, I. E., Nwogboji, E. U., Atolagbe, U. K., Ogbo, O. S., & Bamigbola, P. I. (2026) · doi:10.68854/kg8sas24
- Authors
- Ong, J. C. L., Jin, L., Elangovan, K., Lim, G. Y. S., Lim, D. Y. Z., Sng, G. G. R., Ke, Y. H., Tung, J. Y. M., Zhong, R. J., Koh, C. M. Y., Lee, K. Z. H., Chen, X., Ch’ng, J. K., Than, A., Goh, K. J., Lim, C. P., Ng, T. M., Liu, N., & Ting, D. S. W.
- Affiliations
- Singapore General Hospital (SG)
- SingHealth (SG)
- Tan Tock Seng Hospital (SG)
- Duke-NUS Medical School (SG)
- Corresponding author
- Ting, Daniel Shu Wei
- ORCID
- Venue
- Cell Reports Medicine
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 6
- Issue
- 10
- Pages
- 102323
- ISSN
- 2666-3791 (ISSN-L)
- PMC
- PMC12629785
- OpenAlex
- W4414564500
- Cited by
- 58
- References
- 33
- Citations by year
- 2026: 52 · 2025: 5 · 2024: 1
- FWCI
- 13.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Medicine (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Duke-NUS Medical School (05/FY2022/EX/66-A128, Duke-NUS/RSF/2021/0018, 05/FY2020/EX/15-A58)
- National Medical Research Council (MOH-000655-00, MOH-001014-00, MOH-001689-00)
- Agency for Science, Technology and Research (A20H4g2141, H20C6a0032)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Keywords
- large language models, clinical decision support systems, drug safety, prescribing errors, medication error detection, retrieval-augmented generation, patient safety, medical specialties
- MeSH terms
- Large Language Models, Humans, Language, Medication Errors, Prospective Studies, Cross-Over Studies, Decision Support Systems, Clinical, Patient Safety
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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