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Cited by 24Pharmacovigilance and Adverse Drug ReactionsMachine Learning in HealthcareBiomedical Text Mining and Ontologies

Large Language Models for Adverse Drug Events: A Clinical Perspective

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Zitu, Md Muntasir, Owen, Dwight, Manne, Ashish, Wei, Ping, Li, Lang·2025·Journal of Clinical Medicine
DOI 10.3390/jcm14155490PMC PMC12347610
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Abstract

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows.

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  • Large language models such as ChatGPT, Gemini and DeepSeek could rapidly analyze medication lists and cross-reference known interactions, providing comprehensive and standardized DDI screening at high speed and potentially streamlining preoperative prescribing workflows.

    “Large language models (LLMs) such as Chat-GPT, Gemini and specialized systems like DeepSeek could rapidly analyze medication lists and cross-reference known interactions, providing comprehensive and standardized DDI screening at high speed and potentially streamlining preoperative prescribing workflows [29][30][31][32].”

    From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as [30]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

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  • The use of standardized scenarios ensured reproducibility but may not capture the variability of real-world prescribing.

    “First, as in similar simulation-based studies (Zitu et al., 2025 [30]), the use of standardized scenarios ensured reproducibility but may not capture the variability of real-world prescribing.”

    From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as Zitu et al., 2025 [30]

    Section 2.4 describes the source as a review that categorizes other studies, and Section 5.1 discusses generalizability across clinical data sources. Neither section addresses standardized prescribing scenarios or a reproducibility–real-world variability tradeoff. Searches for simulation, standardized cases, reproducibility, and prescribing found no supporting passage in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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References 58 · Cited by 24

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  1. [2]Zitu M.M. Gatti-Mays M.E. Johnson K.C. Zhang S. Shendre A. Elsaid M.I. Li L. Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model Pragmat. Obs. Res. 2024 15 243 252 10.2147/POR.S468253 39720010 PMC11668329 · DOI
  2. [3]Golder S. O’Connor K. Lopez-Garcia G. Tatonetti N. Gonzalez-Hernandez G. Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use—A Scoping Review medRxiv 2025 10.1101/2025.03.20.25324320 40789734 · DOI
  3. [4]Deng Y. Xing Y. Quach J. Chen X. Wu X. Zhang Y. Moureaud C. Yu M. Zhao Y. Wang L. Developing Large Language Models to Detect Adverse Drug Events in Posts on X J. Biopharm. Stat. 2024 in press 10.1080/10543406.2024.2403442 39300965 · DOI
  4. [5]Murphy R.M. Klopotowska J.E. de Keizer N.F. Jager K.J. Leopold J.H. Dongelmans D.A. Abu-Hanna A. Schut M.C. Adverse Drug Event Detection Using Natural Language Processing: A Scoping Review of Supervised Learning Methods PLoS ONE 2023 18 e0279842 10.1371/journal.pone.0279842 36595517 PMC9810201 · DOI
  5. [6]Field T.S. Gurwitz J.H. Harrold L.R. Rothschild J.M. Debellis K. Seger A.C. Fish L.S. Garber L. Kelleher M. Bates D.W. Strategies for Detecting Adverse Drug Events among Older Persons in the Ambulatory Setting J. Am. Med. Inform. Assoc. 2004 11 492 498 10.1197/jamia.M1586 15299000 PMC524629 · DOI
  6. [8]Sezgin E. Sirrianni J. Linwood S.L. Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model JMIR Med. Inform. 2022 10 e32875 10.2196/32875 35142635 PMC8874824 · DOI
  7. [9]Kopacheva E. Lincke A. Björneld O. Hammar T. Detecting Adverse Drug Events in Clinical Notes Using Large Language Models Stud. Health Technol. Inform. 2025 327 892 893 40380603
  8. [10]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. A Scoping Review on Generative AI and Large Language Models in Mitigating Medication-Related Harm npj Digit. Med. 2025 8 182 10.1038/s41746-025-01565-7 40155703 PMC11953325 · DOI · Paper page
  9. [11]Baumeister R.F. Leary M.R. Writing Narrative Literature Reviews Rev. Gen. Psychol. 1997 1 311 320 10.1037/1089-2680.1.3.311 · DOI · Paper page
  10. [14]Zitu M.M. Le T.D. Duong T. Haddadan S. Garcia M. Amorrortu R. Thieu T. Large Language Models in Cancer: Potentials, Risks, and Safeguards BJR|Artif. Intell. 2025 2 ubae019 10.1093/bjrai/ubae019 39777117 PMC11703354 · DOI
  11. [15]Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A.N. Polosukhin I. Attention Is All You Need Adv. Neural Inf. Process. Syst. 2017 30 6000 6010
  12. [16]Sridharan K. Sivaramakrishnan G. Unlocking the Potential of Advanced Large Language Models in Medication Review and Reconciliation: A Proof-of-Concept Investigation Explor. Res. Clin. Soc. Pharm. 2024 15 100492 10.1016/j.rcsop.2024.100492 39257533 PMC11385755 · DOI · Paper page
  13. [17]Lenehan P. Venkatakrishnan A.J. Murugadoss K. Agarwal V. Singhal A. Karnati C.S.R. Soundararajan V. Clinical nSights: A Software Platform to Accelerate Real-World Oncology Analyses J. Clin. Oncol. 2024 42 e23316 10.1200/JCO.2024.42.16_suppl.e23316 · DOI
  14. [18]Barman H. Venkateswaran S. Del Santo A.D. Yoo U. Silvert E. Rao K. Raghunathan B. Kottschade L.A. Block M.S. Chandler G.S. Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities from Electronic Health Records Using Natural Language Processing. JCO Clin Cancer Inform. 2024 8 e2300151 PMC11161244 38687915
  15. [20]Guillot J. Miao B. Suresh A. Williams C. Zack T. Wolf J.L. Butte A. Constructing Adverse Event Timelines for Patients Receiving CAR-T Therapy Using Large Language Models J. Clin. Oncol. 2024 42 (Suppl. 16) 2555 10.1200/JCO.2024.42.16_suppl.2555 · DOI
  16. [21]Block S. Barman H. Venkateswaran S. Del Santo A.G. Yoo U. Silvert E. Chandler G.S. Wagner T. Mohindra R. The Role of Natural Language Processing Techniques versus Conventional Methods to Gain ICI Safety Insights from Unstructured EHR Data JCO Glob. Oncol. 2023 9 136 10.1200/GO.2023.9.Supplement_1.136 · DOI
  17. [22]Petersen S.K. Madley S. Kjeldsen J. Bastholt L. Ruhlmann C.H. 99P Real-World Management of ir-Colitis: A Danish 10-Year Cohort Study Ann. Oncol. 2024 24 100842 10.1016/j.iotech.2024.100842 · DOI
  18. [24]Sun V. Heemelaar J. Hadzic I. Raghu V. Wu C.Y. Zubiri L. Ghamari A. Suero-Abreu G. Wu J. Hathaway N. Enhancing Early Detection of ICI Myocarditis Cases during Hospitalization: A Role for Large Language Models Circulation 2024 150 (Suppl. 1) 4119426 10.1161/circ.150.suppl_1.4119426 · DOI
  19. [25]Chen S. Guevara M. Ramirez N. Murray A. Warner J.L. Aerts H. Miller T.A. Savova G.K. Mak R.H. Bitterman D.S. Natural Language Processing to Automatically Extract the Presence and Severity of Esophagitis in Notes of Patients Undergoing Radiotherapy JCO Clin. Cancer Inform. 2023 7 e2300048 10.1200/CCI.23.00048 37506330 · DOI · Paper page
  20. [26]Andrade G.H.B. Nishiyama T. Fujimaki T. Yada S. Wakamiya S. Takagi M. Kato M. Miyashiro I. Aramaki E. Assessing Domain Adaptation in Adverse Drug Event Extraction on Real-World Breast Cancer Records Int. J. Med. Inform. 2024 191 105539 10.1016/j.ijmedinf.2024.105539 39084086 · DOI
  21. [27]Kawazoe Y. Shimamoto K. Seki T. Tsuchiya M. Shinohara E. Yada S. Wakamiya S. Imai S. Hori S. Aramaki E. Post-Marketing Surveillance of Anticancer Drugs Using Natural Language Processing of Electronic Medical Records npj Digit. Med. 2024 7 315 10.1038/s41746-024-01323-1 39521935 PMC11550814 · DOI
  22. [28]Nishiyama T. Yamaguchi A. Han P. Pereira L.W.K. Otsuki Y. Andrade G.H.B. Kudo N. Yada S. Wakamiya S. Aramaki E. Automated System to Capture Patient Symptoms from Multitype Japanese Clinical Texts: Retrospective Study JMIR Med. Inform. 2024 12 e58977 10.2196/58977 39316418 PMC11462096 · DOI
  23. [29]Yanagisawa Y. Watabe S. Yokoyama S. Sayama K. Kizaki H. Tsuchiya M. Imai S. Someya M. Taniguchi R. Yada S. Identifying Adverse Events in Outpatients with Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition JMIR Cancer 2025 11 e69663 10.2196/69663 40068144 PMC11937706 · DOI
  24. [30]Dai X. Karimi S. Sarker A. Hachey B. Paris C. MultiADE: A Multi-Domain Benchmark for Adverse Drug Event Extraction J. Biomed. Inform. 2024 160 104744 10.1016/j.jbi.2024.104744 39536999 · DOI
  25. [31]Tavabi N. Pruneski J. Golchin S. Singh M. Sanborn R. Heyworth B. Landschaft A. Kimia A. Kiapour A. Building Large-Scale Registries from Unstructured Clinical Notes Using a Low-Resource Natural Language Processing Pipeline Artif. Intell. Med. 2024 151 102847 10.1016/j.artmed.2024.102847 38658131 · DOI
  26. [32]Zitu M.M. Zhang S. Owen D.H. Chiang C. Li L. Generalizability of Machine Learning Methods in Detecting Adverse Drug Events from Clinical Narratives in Electronic Medical Records Front. Pharmacol. 2023 14 1218679 10.3389/fphar.2023.1218679 37502211 PMC10368879 · DOI
  27. [33]Chaichulee S. Promchai C. Kaewkomon T. Kongkamol C. Ingviya T. Sangsupawanich P. Multi-Label Classification of Symptom Terms from Free-Text Bilingual Adverse Drug Reaction Reports Using Natural Language Processing PLoS ONE 2022 17 e0270595 10.1371/journal.pone.0270595 35925971 PMC9352066 · DOI
  28. [34]Fan Y. Zhou S. Li Y. Zhang R. Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text J. Am. Med. Inform. Assoc. 2021 28 569 577 10.1093/jamia/ocaa218 33150942 PMC7936508 · DOI
  29. [35]Hu Y. Chen Q. Du J. Peng X. Keloth V.K. Zuo X. Zhou Y. Li Z. Jiang X. Lu Z. Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering J. Am. Med. Inform. Assoc. 2024 31 1812 1820 10.1093/jamia/ocad259 38281112 PMC11339492 · DOI · Paper page
  30. [36]Koon Y.L. Lam Y.T. Tan H.X. Teo D.H.C. Neo J.W. Yap A.J.Y. Ang P.S. Loke C.P.W. Tham M.Y. Tan S.H. Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore Drug Saf. 2025 48 667 677 10.1007/s40264-025-01525-w 39982676 · DOI
  31. [37]Li F. Jin Y. Liu W. Rawat B.P.S. Cai P. Yu H. Fine-Tuning Bidirectional Encoder Representations from Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study JMIR Med. Inform. 2019 7 e14830 10.2196/14830 31516126 PMC6746103 · DOI · Paper page
  32. [38]Mahendran D. McInnes B.T. Extracting Adverse Drug Events from Clinical Notes AMIA Jt. Summits Transl. Sci. Proc. 2021 2021 420 429 34457157 PMC8378605
  33. [39]McMaster C. Chan J. Liew D.F.L. Su E. Frauman A.G. Chapman W.W. Pires D.E.V. Developing a Deep Learning Natural Language Processing Algorithm for Automated Reporting of Adverse Drug Reactions J. Biomed. Inform. 2023 137 104265 10.1016/j.jbi.2022.104265 36464227 · DOI
  34. [40]Silverman A.L. Sushil M. Bhasuran B. Ludwig D. Buchanan J. Racz R. Parakala M. El-Kamary S. Ahima O. Belov A. Algorithmic Identification of Treatment-Emergent Adverse Events from Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease Clin. Pharmacol. Ther. 2024 115 1391 1399 10.1002/cpt.3226 38459719 PMC11090709 · DOI
  35. [41]Guan H. Devarakonda M. Leveraging Contextual Information in Extracting Long-Distance Relations from Clinical Notes AMIA Annu. Symp. Proc. 2020 2019 1051 1060 32308902 PMC7153124
  36. [42]Mitra A. Rawat B.P.S. McManus D. Kapoor A. Yu H. Bleeding Entity Recognition in Electronic Health Records: A Comprehensive Analysis of End-to-End Systems AMIA Annu. Symp. Proc. 2021 2020 860 869 33936461 PMC8075442
  37. [43]Rawat B.P.S. Jagannatha A. Liu F. Yu H. Inferring ADR Causality by Predicting the Naranjo Score from Clinical Notes AMIA Annu. Symp. Proc. 2021 2020 1041 1049 33936480 PMC8075501
  38. [44]Stanekova V. Inglis J.M. Lam L. Lam A. Smith W. Shakib S. Bacchi S. Improving the Performance of Machine Learning Penicillin Adverse Drug Reaction Classification with Synthetic Data and Transfer Learning Intern. Med. J. 2024 54 1183 1189 10.1111/imj.16360 38482918 · DOI
  39. [45]Wu H. Ji J. Tian H. Chen Y. Ge W. Zhang H. Yu F. Zou J. Nakamura M. Liao J. Chinese-Named Entity Recognition from Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding-Based BERT in a Bi-Directional Long Short-Term Conditional Random Field Model JMIR Med. Inform. 2021 9 e26407 10.2196/26407 34855616 PMC8686410 · DOI
  40. [46]Wu J. Ruan X. McNeer E. Rossow K.M. Choi L. Developing a Natural Language Processing System Using Transformer-Based Models for Adverse Drug Event Detection in Electronic Health Records medRxiv 2024 10.1101/2024.07.09.24310100 PMC13322497 42384713 · DOI
  41. [47]Jeong E. Su Y. Li L. Chen Y. Discovering Severe Adverse Reactions from Pharmacokinetic Drug–Drug Interactions through Literature Analysis and Electronic Health Record Verification Clin. Pharmacol. Ther. 2025 117 1078 1087 10.1002/cpt.3500 39585167 PMC11924148 · DOI
  42. [48]Ma J. Chen H. Sun J. Huang J. He G. Yang G. Efficient Analysis of Drug Interactions in Liver Injury: A Retrospective Study Leveraging Natural Language Processing and Machine Learning BMC Med. Res. Methodol. 2024 24 312 10.1186/s12874-024-02443-8 39707270 PMC11660714 · DOI
  43. [49]Kumar V. Rasouliyan L. Wang Y. Aggarwal P. Long S. Reasons for Discontinuation of Ozempic Using a Natural Language Processing Pipeline on Unstructured Clinical Notes Value Health 2024 27 S441 10.1016/j.jval.2024.10.2254 · DOI
  44. [50]Wang Y. Rasouliyan L. Kumar V. Reasons for Biologic Treatment Alteration in Inflammatory Bowel Disease: Insights from Unstructured Clinical Notes Derived from Large Language Models Value Health 2024 27 S592 10.1016/j.jval.2024.10.3656 · DOI
  45. [51]Washington N. Telis N. Judge D. Pawloski P. Grzymski J. Chahal A. Hajek C. Kruisselbrink T. Cirulli E. Retrospective Study Shows Pharmacogenomic Testing Could Reduce Adverse Events Associated with Clopidogrel Use by 38% Circulation 2024 150 (Suppl. 1) 4147064 10.1161/circ.150.suppl_1.4147064 · DOI
  46. [52]Gallifant J. Celi L.A. Sharon E. Bitterman D.S. Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance JCO Clin. Cancer Inform. 2024 8 e2400051 10.1200/CCI.24.00051 38713889 PMC11466373 · DOI
  47. [54]Matheny M.E. Yang J. Smith J.C. Walsh C.G. Al-Garadi M.A. Davis S.E. Marsolo K.A. Fabbri D. Reeves R.R. Johnson K.B. Enhancing Postmarketing Surveillance of Medical Products with Large Language Models JAMA Netw. Open 2024 7 e2428276 10.1001/jamanetworkopen.2024.28276 39150707 · DOI
  48. [55]Zitu M.M. Li L. Elsaid M.I. Gatti-Mays M.E. Manne A. Shendre A. Comparative Assessment of Manual Chart Review and Patient-Level Adverse Drug Event Identification Using Artificial Intelligence in Evaluating Immunotherapy-Related Adverse Events (irAEs) J. Clin. Oncol. 2023 41 e13583 10.1200/JCO.2023.41.16_suppl.e13583 · DOI

Authors
Zitu, M. M., Owen, D., Manne, A., Wei, P., & Li, L.
Affiliations
  • Moffitt Cancer Center (US)
  • The Ohio State University (US)
Corresponding author
Li, Lang
ORCID
Venue
Journal of Clinical Medicine
Publisher
MDPI AG
Published
2025-08-04
Volume
14
Issue
15
Pages
5490
ISSN
2077-0383 (ISSN-L)
DOI
10.3390/jcm14155490
PMC
PMC12347610
OpenAlex
W4412954310
Cited by
24
References
58
Citations by year
2026: 18 · 2025: 6
FWCI
4.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Clinical Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
adverse drug reactions, large language models, pharmacovigilance, clinical decision support, clinical text mining, immune-related adverse events, personalized decision support, chatbots, medication adherence, patient safety, interpretability, data privacy

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