AldermanJEPalmerJLawsE, et al. Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digit Health. 2025;7(1):e64–e88. doi:10.1016/S2589-7500(24)00224-3.
2.
BurgerGAbu-HannaAde KeizerN, et al. Natural language processing in pathology: a scoping review. J Clin Pathol. 2016;69(11):949–955. doi:10.1136/jclinpath-2016-203872.
3.
CharyMParikhSManiniAF, et al. A review of natural language processing in medical education. West J Emerg Med. 2019;20(1):78–86. doi:10.5811/westjem.2018.11.39725.
4.
ChatterjeeSDavisonRM.The need for compelling problematisation in research: the prevalence of the gap-spotting approach and its limitations. Inf Syst J. 2021;31(2):227–230. doi:10.1111/isj.12316.
5.
ChengJ.Applications of large language models in pathology. Bioengineering. 2024;11(4):342. doi:10.3390/bioengineering11040342.
6.
CorradiMLuechtefeldTde HaanAM, et al. The application of natural language processing for the extraction of mechanistic information in toxicology. Front Toxicol. 2024;6:1393662. doi:10.3389/ftox.2024.1393662.
7.
DaveTAthaluriSASinghS.ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595. doi:10.3389/frai.2023.1169595.
8.
DaviesHNenadicGAlfattniG, et al. Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words. Front Vet Sci. 2024;11:1352239. doi:10.3389/fvets.2024.1352239.
9.
DuWHarrisJCBrunettiA, et al. Large language models in pathology: a comparative study on multiple choice question performance with pathology trainees. Ann Diagn Pathol. 2024;73:152392. doi:10.1101/2024.07.10.24310093.
10.
HammamiLPaglialongaAPruneriG, et al. Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: a rule-based approach. J Biomed Inf. 2021;116:103712. doi:10.1016/j.jbi.2021.103712.
11.
HurBHardefeldtLVerspoorK, et al. Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia. Aust Vet J. 2019;97(8):298–300. doi:10.1111/avj.12836.
12.
KarabacakMMargetisK.Embracing large language models for medical applications: opportunities and challenges. Cureus. 2023;15(5):e39305. doi:10.7759/cureus.39305.
13.
KianianRSunDCrowellEL, et al. The use of large language models to generate education materials about uveitis. Ophthalmol Retina. 2024;8:195–201. doi:10.1016/j.oret.2023.09.008.
14.
KreimeyerKFosterMPandeyA, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform. 2017;73:14–29. doi:10.1016/j.jbi.2017.07.012.
15.
LamHNguyenFWangX, et al. An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports. J Pathol Inform. 2022;13:100154. doi:10.1016/j.jpi.2022.100154.
16.
LhoestQdel MoralAVJerniteY, et al. Datasets: a community library for natural language processing. arXiv. 2021. doi:10.48550/arXiv.2109.02846.
17.
LitjensGKooiTBejnordiBE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi:10.1016/j.media.2017.07.005.
18.
LiuZRobertsRALal-NagM, et al. AI-based language models powering drug discovery and development. Drug Discovery Today. 2021;26(11):2593–2607. doi:10.1016/j.drudis.2021.06.009.
19.
López-ÚbedaPMartín-NoguerolTAneiros-FernándezJ, et al. Natural language processing in pathology: current trends and future insights. Am J Pathol. 2022;192:1486–1495. doi:10.1016/j.ajpath.2022.07.012.
20.
LuMYChenBWilliamsonDFK, et al. A multimodal generative AI copilot for human pathology. Nature. 2024;634:466–473. doi:10.1038/s41586-024-07618-3.
21.
MohammadBSuptiTAlzubaidiM, et al. The pros and cons of using ChatGPT in medical education: a scoping review. Stud Health Technol Inform. 2023;305:644–647. doi:10.3233/SHTI230580.
22.
OmarMNassarSSharIfK, et al. Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review. Front Med. 2025;11:1512824. doi:10.3389/fmed.2024.1512824.
23.
PengDZhengLLiuD, et al. Large-language models facilitate discovery of the molecular signatures regulating sleep and activity. Nat Commun. 2024;15(1):3685. doi:10.1038/s41467-024-48005-w.
24.
Petit-JeanTGérardinCBerthelotE, et al. Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions. J Am Med Inform Assoc. 2024;31(6):1280–1290. doi:10.1093/jamia/ocae069.
25.
PusicMVHallEBillingsH, et al. Educating for adaptive expertise: case examples along the medical education continuum. Adv Health Sci Educ Theory Pract. 2022;27(5):1383–1400. doi:10.1007/s10459-022-10165-z.
26.
RasouliSAlkurdiDJiaB.The role of artificial intelligence in modern medical education and practice: a systematic literature review. medRxiv. 2024. doi:10.1101/2024.07.25.24311022.
27.
SandbergJAlvessonM.Ways of constructing research questions: gap-spotting or problematization?Organization. 2010;18:23–44. doi:10.1177/1350508410372151.
28.
SandeepFKiranNRahamanZ, et al. Pathology in the age of artificial intelligence (AI): redefining roles and responsibilities for tomorrow’s practitioners. Cureus. 2024;16(3):e56040. doi:10.7759/cureus.56040.
29.
SinghalKAziziSTuT, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172–180. doi:10.1038/s41586-023-06291-2.
30.
SzlosekDAFerrettJ.Using machine learning and natural language processing algorithms to automate the evaluation of clinical decision support in electronic medical record systems. EGEMS (Wash DC). 2016;4(3):1222. doi:10.13063/2327-9214.1222.
31.
VenkataramanGRPinedaALIvOJBDW, et al. FasTag: automatic text classification of unstructured medical narratives. PLoS ONE. 2020;15(6):e0234647. doi:10.1371/journal.pone.0234647.
32.
WangDZhangS.Large language models in medical and healthcare fields: applications, advances, and challenges. Artif Intell Rev. 2024;57(11):299. doi:10.1007/s10462-024-10921-0.
