ChatGPT-5 for drug–drug interaction detection in the intensive care unit: a real-world cohort study on large language model advances and implications for clinical pharmacists

Multicollinearity and misleading statistical results

Kim, J. (2019) · Korean Journal of Anesthesiology · doi:10.4097/kja.19087 · Cited by 3,032 · see citing works

Evaluation and mitigation of the limitations of large language models in clinical decision-making

Hager, P., Jungmann, F., Holland, R., Bhagat, K., Hubrecht, I., Knauer, M., Vielhauer, J., Makowski, M., Braren, R., Kaissis, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03097-1 · Cited by 798 · see citing works

ChatGPT and the future of medical writing

Biswas, S. (2023) · Radiology · doi:10.1148/radiol.223312 · Cited by 571 · see citing works

Current applications and challenges in large language models for patient care: a systematic review

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Sociodemographic biases in medical decision making by large language models

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Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis

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Using ChatGPT-4 to create structured medical notes from audio recordings of physician-patient encounters: comparative study

Kernberg, A., Gold, J. A., & Mohan, V. (2024) · Journal of Medical Internet Research · doi:10.2196/54419 · Cited by 120 · see citing works

Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation

Ntinopoulos, V., Biefer, H. R. C., Tudorache, I., Papadopoulos, N., Odavic, D., Risteski, P. S., Haeussler, A., & Dzemali, O. (2025) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101139 · Cited by 93 · see citing works

A scoping review on generative AI and large language models in mitigating medication related harm

Ong, J. C. L., Chen, M. H., Ng, N., Elangovan, K., Tan, N. Y. T., Jin, L., Xie, Q., Ting, D. S. W., Rodriguez-Monguio, R., Bates, D. W., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01565-7 · Cited by 64 · see citing works

Automated generation of discharge summaries: leveraging large language models with clinical data

Ganzinger, M., Kunz, N., Fuchs, P., Lyu, C. K., Loos, M., Dugas, M., & Pausch, T. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-01618-7 · Cited by 56 · see citing works

Overall performance of a drug–drug interaction clinical decision support system: quantitative evaluation and end-user survey

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Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians

Moëll, B., & Aronsson, F. S. (2025) · Journal of Medical Internet Research · doi:10.2196/75849 · Cited by 50 · see citing works

Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients

Dsouza, V. S., Leyens, L., Kurian, J. R., Brand, A. M., & Brand, H. (2025) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2025.02.008 · Cited by 48 · see citing works

Clinically relevant potential drug-drug interactions in intensive care patients: A large retrospective observational multicenter study

Bakker, T., Abu–Hanna, A., Dongelmans, D. A., Vermeijden, W., Bosman, R. J., de Lange, D. W., Klopotowska, J. E., de Keizer, N. F., Hendriks, S. A., Cate, J. T. M. H. T., et al. (2020) · Journal of Critical Care · doi:10.1016/j.jcrc.2020.11.020 · Cited by 46 · see citing works

Large language models in critical care

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Role of artificial intelligence in pharmacy practice: A narrative review

Wong, A., Wentz, E. E., Palisano, N., Dirani, M., Elsamadisi, P., Qashou, F., Celi, L. A., Badawi, O., & Nazer, L. H. (2023) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.1856 · Cited by 35 · see citing works

Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study

Franc, J. M., Hertelendy, A. J., Cheng, L. T. W., Hata, R., & Verde, M. (2024) · Journal of Medical Internet Research · doi:10.2196/55648 · Cited by 30 · see citing works

Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data

Radha Krishnan, R. P., Hung, E. H., Ashford, M., Edillo, C. E., Gardner, C., Hatrick, H. B., Kim, B., Lai, A. W. Y., Li, X., Zhao, Y. X., et al. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.16275 · Cited by 29 · see citing works

Can large language models detect drug–drug interactions leading to adverse drug reactions?

Sicard, J., Montastruc, F., Achalme, C., Jonville-Bera, A. P., Songue, P., Babin, M., Soeiro, T., Schiro, P., de Canecaude, C., & Barus, R. (2025) · Therapeutic Advances in Drug Safety · doi:10.1177/20420986251339358 · Cited by 29 · see citing works

The magnitude of mortality and its predictors among adult patients admitted to the Intensive care unit in Amhara Regional State, Northwest Ethiopia

Demass, T. B., Guadie, A. G., Mengistu, T. B., Belay, Z. A., Melese, A. A., Berneh, A. A., Gedefaw, L., Wagaye, F. E., & Bantie, G. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-39190-7 · Cited by 27 · see citing works

Students’ acceptance and use of generative AI in pharmacy education: international cross-sectional survey based on the extended unified theory of acceptance and use of technology

Elnaem, M. H., Okuyan, B., Mubarak, N., Thabit, A. K., AbouKhatwa, M. M., Ramatillah, D. L., Isah, A., Al‐Jumaili, A. A., & Nazar, N. I. M. (2025) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-025-01936-w · Cited by 27 · see citing works

Artificial intelligence for clinical reasoning: the reliability challenge and path to evidence-based practice

Xu, H., Wang, Y., Xun, Y., Shao, R., & Jiao, Y. (2025) · QJM · doi:10.1093/qjmed/hcaf114 · Cited by 21 · see citing works

Assessing the performance of large language models in literature screening for pharmacovigilance: a comparative study

Li, D., Wu, L., Zhang, M., Shpyleva, S., Lin, Y.-C., Huang, H., Li, T., & Xu, J. (2024) · Frontiers in Drug Safety and Regulation · doi:10.3389/fdsfr.2024.1379260 · Cited by 19 · see citing works

The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy

Busser, B. D., Roth, L., & Loof, H. D. (2024) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-024-01839-2 · Cited by 19 · see citing works

A pilot feasibility study comparing large language models in extracting key information from ICU patient text records from an Irish population

Urquhart, E., Ryan, J., Hartigan, S., Nita, C., Hanley, C., Moran, P., Bates, J., Jooste, R., Judge, C., Laffey, J. G., et al. (2024) · Intensive Care Medicine Experimental · doi:10.1186/s40635-024-00656-1 · Cited by 18 · see citing works

Accuracy and reproducibility of ChatGPT responses to real‐world drug information questions

Khatri, S., Sengul, A., Moon, J., & Jackevicius, C. A. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70038 · Cited by 18 · see citing works

Risk stratification of potential drug interactions involving common over-the-counter medications and herbal supplements by a large language model

Kim, J., Kincaid, J. W. R., Rao, A. S., Lie, W., Fuh, L., Landman, A., & Succi, M. D. (2024) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2024.102304 · Cited by 14 · see citing works

Sentiment analysis in medication adherence: using ruled-based and artificial intelligence-driven algorithms to understand patient medication experiences

Bottacin, W. E., Luquetta, A., Gomes-Jr, L., de Souza, T. T., Reis, W. C. T., & Melchiors, A. C. (2024) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-024-01803-0 · Cited by 11 · see citing works

Impact of artificial intelligence on future clinical pharmacy research and scholarship

Chan, A., Baker, W. L., Abazia, D. T., Bauman, J. L., DeVane, C. L., Goodlet, K. J., Hall, N., Hicks, J. K., Jones, E., Lu, C., et al. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70003 · Cited by 11 · see citing works

ChatGPT’s Attitude, Knowledge, and Clinical Application in Geriatrics Practice and Education: Exploratory Observational Study

Cheng, H. Y. (2024) · JMIR Formative Research · doi:10.2196/63494 · Cited by 9 · see citing works

Diagnostic performance of newly developed large language models in critical illness cases: A comparative study

Wu, X., Huang, Y., & He, Q. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106088 · Cited by 8 · see citing works

Intentions of hospital pharmacists to use digital technology in their daily practice: a cross-sectional survey using the Theory of Planned Behaviour

Tecen-Yücel, K., Ozdemir-Ayduran, N., Kara, E., et al. (2025) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-025-01868-5 · Cited by 7 · see citing works

An assessment of generative artificial intelligence in responding to clinical queries on tapering antidepressants

Oscar, M. M., Boland, M., & Cadogan, C. A. (2025) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2025.06.107 · Cited by 4 · see citing works

Harnessing ChatGPT for digital tools in pharmacy practice

Yakob, R. A., Bamgboje‐Ayodele, A., Collins, J. C., & Aslani, P. (2025) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2025.06.106 · Cited by 3 · see citing works

Artificial intelligence is here, but are we ready?

Nguyen, K. A., & Zillich, A. J. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70021 · 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