cirpublications.com-jnzH-1790880682-q456290795 1790880682 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2025 5 1 Ivan Petrov Olga Ivanova Dmitry Smirnov 2025 10.68159/q456290795 https://cirpublications.com/pub/journal/2/article/q456290795 Fong A, Harriott N, Walters DM, Foley H, Morrissey R, Ratwani RR. Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events. Int J Med Inform. 2017;104:120-5. Wang Y, Coiera E, Runciman W, Magrabi F. Using multiclass classification to automate the identification of patient safety incident reports by type and severity. BMC Med Inform Decis Mak. 2017;17(1):84. Wang Y, Coiera E, Magrabi F. Using convolutional neural networks to identify patient safety incident reports by type and severity. J Am Med Inform Assoc. 2019;26(12):1600-8. Young IJ, Luz S, Lone N. A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. Int J Med Inform. 2019;132:103971. Murphy DR, Savoy A, Satterly T, Sittig DF, Singh H. Dashboards for visual display of patient safety data: a systematic review. BMJ Health Care Inform. 2021;28(1):e100437. Tetzlaff L, Heinrich AS, Schadewitz R, Thomeczek C, Schrader T. Die Analyse des CIRSmedical.de mittels Natural Language Processing. Z Evid Fortbild Qual Gesundhwes. 2022;169:1-1. Ozonoff A, Milliren CE, Fournier K, Welcher J, Landschaft A, Samnaliev M, et al. Electronic surveillance of patient safety events using natural language processing. Health Informatics J. 2022;28(4):14604582221132429. Large Language Models in medicine: Thirunavukarasu AJ, Ting DS, Elangovan K, et al. Large language models in medicine. Nat Med. 2023;29(8):1930-40. Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172-80. Liu J, Wang C, Liu S. Utility of ChatGPT in clinical practice. J Med Internet Res. 2023;25:e48568. Dave T, Athaluri SA, Singh S. ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595. Rao A, Pang M, Kim J, Kamineni M, Lie W, Prasad AK, et al. Assessing the utility of ChatGPT throughout the entire clinical workflow: development and usability study. J Med Internet Res. 2023;25:e48659. Baker HP, Dwyer E, Kalidoss S, Hynes K, Wolf J, Strelzow JA. ChatGPT's ability to assist with clinical documentation: a randomized controlled trial. JAAOS. 2024;32(3):123-9. Yim WW, Fu Y, Ben Abacha A, Snider N, Lin T, Yetisgen M. ACI-Bench: a novel ambient clinical intelligence dataset for benchmarking automatic visit note generation. Sci Data. 2023;10(1):586. Fong A. Realizing the power of text mining and natural language processing for analyzing patient safety event narratives: the challenges and path forward. J Patient Saf. 2021;17(8):e834-6. Tabaie A, Sengupta S, Pruitt ZM, Fong A. A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ Health Care Inform. 2023;30(1):e100731. Evans HP, Anastasiou A, Edwards A, Hibbert P, Makeham M, Luz S, et al. Automated classification of primary care patient safety incident report content and severity using supervised machine learning approaches. Health Informatics J. 2020;26(4):3123-39. Wang Y, Coiera E, Magrabi F. Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity? J Am Med Inform Assoc. 2020;27(10):1502-9. Boxley C, Fujimoto M, Ratwani RM, Fong A. A text mining approach to categorize patient safety event reports by medication error type. Sci Rep. 2023;13(1):18354. Tang L, Sun Z, Idnay B, Nestor JG, Soroush A, Elias PA, et al. Evaluating large language models on medical evidence summarization. NPJ Digit Med. 2023;6(1):158. Oniani D, Hilsman J, Peng Y, Poropatich RK, Pamplin JC, Legault GL, et al. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digit Med. 2023;6(1):225. Clusmann J, Kolbinger FR, Muti HS, Carrero ZI, Eckardt JN, Laleh NG, et al. The future landscape of large language models in medicine. Commun Med (Lond). 2023;3(1):141. Chen H, Cohen E, Wilson D, Alfred M. A machine learning approach with human-AI collaboration for automated classification of patient safety event reports: algorithm development and validation study. JMIR Hum Factors. 2024;11(1):e53378. Denecke K, Paula H. Analysis of critical incident reports using natural language processing. Ind Health. 2024:1-6. Tabaie A, Tran A, Calabria T, Bennett SS, Milicia A, Weintraub W, et al. Evaluation of a natural language processing approach to identify diagnostic errors and analysis of safety learning system case review data: retrospective cohort study. J Med Internet Res. 2024;26:e50935. Heilmeyer F, Böhringer D, Reinhard T, Arens S, Lyssenko L, Haverkamp C. Viability of open large language models for clinical documentation in German health care: real-world model evaluation study. JMIR Med Inform. 2024;12:e59617. Hölzing CR, Rumpf S, Huber S, Papenfuß N, Meybohm P, Happel O. The potential of using generative AI/NLP to identify and analyse critical incidents in a Critical Incident Reporting System (CIRS): a feasibility case-control study. Healthcare (Basel). 2024;12(19):1964. Moulaei K, Yadegari A, Baharestani M, Farzanbakhsh S, Sabet B, Afrash MR. Generative artificial intelligence in healthcare: a scoping review on benefits, challenges and applications. Int J Med Inform. 2024;188:105474. Omar M, Nadkarni GN, Klang E, Glicksberg BS. Large language models in medicine: a review of current clinical trials across healthcare applications. PLOS Digit Health. 2024;3(11):e0000662.