Artificial intelligence language models for medical text analysis: A systematic review
Abstract
Medical text records serve as essential repositories of patient information, providing a foundation for informed clinical decision-making, accurate diagnosis, reliable prognosis, and effective treatment planning. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) and Machine Learning (ML), have positioned AI-driven language models as powerful tools for analyzing, classifying, and generating medical textual data. In this systematic literature review, an initial search retrieved 548 records published between 1 January 2000 and 1 July 2024. After rigorous screening based on predefined inclusion and exclusion criteria, 22 original research articles were included. The review highlights substantial progress in applying advanced architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) to medical text processing tasks. These models consistently outperform conventional NLP and ML approaches, achieving superior results in disease classification, automated clinical documentation, and predictive analytics. However, critical challenges persist, including the limited availability of clinically validated datasets, variability in data preprocessing protocols, insufficient external validation, and the lack of interpretable AI frameworks, all of which collectively hinder clinical trust and large-scale adoption. Future research should prioritize the development of hybrid AI systems that integrate multimodal data sources (text, imaging, and structured records), incorporate explainable AI mechanisms, and adhere to standardized reporting frameworks. Addressing these methodological gaps will be pivotal in enhancing the reliability, clinical applicability, and impact of AI language models, thereby advancing evidence-based medicine, personalized treatment strategies, and overall patient care.
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Existing reviews establish the technical feasibility and performance of healthcare LLMs, but mostly overlook the transparency and interpretability mechanisms required for regulatory approval, clinician acceptance, and patient safety.
“While existing reviews establish the technical feasibility and performance of healthcare LLMs [50], they mostly overlook the transparency and interpretability mechanisms required for regulatory approval, clinician acceptance, and patient safety.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [50]
Cites
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718 · see citing works
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · see citing works
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018 · see citing works
A large language model for electronic health records
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847 · see citing works
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 748 · see citing works
A systematic review of large language model (LLM) evaluations in clinical medicine
Shool, S., Adimi, S., Saboori Amleshi, R., Bitaraf, E., Golpira, R., & Tara, M. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02954-4 · Cited by 346 · see citing works
Ethical and regulatory challenges of large language models in medicine
Ong, J. C. L., Chang, S. Y.-H., William, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi-Velez, F., Lu, W., Savulescu, J., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00061-x · Cited by 342 · see citing works
Neural Natural Language Processing for unstructured data in electronic health records: A review
Li, I., Pan, J. N., Goldwasser, J., Verma, N., Wong, W. P., Nuzumlalı, M. Y., Rosand, B., Li, Y., Zhang, M. M., Chang, D., et al. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100511 · Cited by 262 · see citing works
Large language models in medical and healthcare fields: applications, advances, and challenges
Wang, D., & Zhang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10921-0 · Cited by 246 · see citing works
A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM
Miah, M. S. U., Kabir, M. M., Sarwar, T. B., Safran, M., Alfarhood, S., & Mridha, M. F. (2024) · Scientific Reports · doi:10.1038/s41598-024-60210-7 · Cited by 210 · see citing works
Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K.-L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102412 · Cited by 207 · see citing works
The potential impact of ChatGPT in clinical and translational medicine
Xue, V. W., Lei, P., & Cho, W. C. S. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1216 · Cited by 198 · see citing works
Machine learning towards intelligent systems: applications, challenges, and opportunities
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09948-w · Cited by 168 · see citing works
A medical multimodal large language model for future pandemics
Liu, F., Zhu, T., Wu, X., Yang, B., You, C., Wang, C., Lu, L., Liu, Z., Zheng, Y., Sun, X., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00952-2 · Cited by 155 · see citing works
Nøhr, C. G., Parv, L., Kink, P., Cummings, E. A., Almond, H. M., Nørgaard, J. R., & Turner, P. A. (2017) · BMC Health Services Research · doi:10.1186/s12913-017-2482-y · Cited by 121 · see citing works
Diallo, R., Edalo, C., & Awe, O. O. (2024) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-72215-8_12 · Cited by 107 · see citing works
Natural language generation for electronic health records
Lee, S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0070-0 · Cited by 88 · see citing works
Machine learning models for chronic kidney disease diagnosis and prediction
Rahman, M. M., Al-Amin, M., & Hossain, M. J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105368 · Cited by 82 · see citing works
Modified Brier score for evaluating prediction accuracy for binary outcomes
Yang, W., Jiang, J.-K., Schnellinger, E., Kimmel, S., & Guo, W. (2022) · Statistical Methods in Medical Research · doi:10.1177/09622802221122391 · Cited by 61 · see citing works
A knowledge discovery and reuse pipeline for information extraction in clinical notes
Patrick, J. D., Nguyen, D. H., Wang, Y., & Li, M. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000302 · Cited by 59 · see citing works
Rikan, S. B., Azar, A. S., Naemi, A., Bagherzadeh, J., Pirnejad, H., & Wiil, U. K. (2024) · Scientific Reports · doi:10.1038/s41598-024-53006-2 · Cited by 59 · see citing works
Self-citationSeo, J., Choi, D., Kim, T., Cha, W. C., Kim, M., Yoo, H., Oh, N., Yi, Y., Lee, K. H., & Choi, E. (2024) · Journal of Medical Internet Research · doi:10.2196/58329 · Cited by 54 · see citing works
de Hond, A., Leeuwenberg, T., Bartels, R., van Buchem, M., Kant, I., Moons, K. G., & van Smeden, M. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00111-0 · Cited by 53 · see citing works
A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
Alruily, M., El-Ghany, S. A., Mostafa, A. M., Ezz, M. M., & El-Aziz, A. A. A. (2023) · Applied Sciences · doi:10.3390/app13085047 · Cited by 49 · see citing works
Chen, W., Lü, Z., You, L., Zhou, L., Xu, J., & Chen, K. (2020) · JMIR Medical Informatics · doi:10.2196/18186 · Cited by 46 · see citing works
Faris, H., Habib, M., Faris, M., Elayan, H., & Alomari, A. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100513 · Cited by 40 · see citing works
Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, L., He, M., & Ge, Z. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104281 · Cited by 39 · see citing works
de Hond, A., van Buchem, M. M., Fanconi, C., Roy, M., Blayney, D. W., Kant, I. M. J., Steyerberg, E. W., & Hernandez‐Boussard, T. (2024) · JMIR Medical Informatics · doi:10.2196/51925 · Cited by 30 · see citing works
GenHPF: General Healthcare Predictive Framework for Multi-Task Multi-Source Learning
Hur, K., Oh, J., Kim, J., Kim, J., Lee, M. J., Cho, E., Moon, S.-E., Kim, Y., Atallah, L., & Choi, E. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3327951 · Cited by 29 · see citing works
Review of National-Level Personal Health Records in Advanced Countries
Lee, J., Park, Y. R., Park, Y. R., & Lee, J. (2021) · Healthcare Informatics Research · doi:10.4258/hir.2021.27.2.102 · Cited by 27 · see citing works
Sirrianni, J., Sezgın, E., Claman, D. B., & Linwood, S. L. (2022) · Methods of Information in Medicine · doi:10.1055/a-1900-7351 · Cited by 27 · see citing works
Li, Z., Kormilitzin, A., Fernandes, M., Vaci, N., Liu, Q., Newby, D., Goodday, S., Smith, T. D., Nevado‐Holgado, A. J., & Winchester, L. M. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104704 · Cited by 26 · see citing works
Chen, P., Chen, L., Lin, Y.-K., Li, G.-H., Lai, F., Lü, C.-W., Yang, C.-Y., Chen, K., & Lin, T. (2022) · JMIR Medical Informatics · doi:10.2196/38241 · Cited by 26 · see citing works
Agarwal, K., Choudhury, S., Tipirneni, S., Mukherjee, P., Ham, C., Tamang, S. R., Baker, M. C., Tang, S., Kocaman, V., Gevaert, O., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-13072-w · Cited by 26 · see citing works
Shivananjaiah, S. K. J., Kumari, S., Majid, I., & Wang, S. Y. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1157016 · Cited by 25 · see citing works
Multimodal fine-tuning of clinical language models for predicting COVID-19 outcomes
Henriksson, A., Pawar, Y., Hedberg, P., & Nauclér, P. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102695 · Cited by 20 · see citing works
Ebrahimi, A., Henriksen, M. B., Brasen, C. L., Hilberg, O., Hansen, T. F., Jensen, L. H., Peimankar, A., & Wiil, U. K. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02231-4 · Cited by 20 · see citing works
Self-citationShammi, S. A., Ghosh, P., Sutradhar, A., Shamrat, F. M. J. M., Moni, M. A., & de Oliveira, T. E. A. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3449748 · Cited by 18 · see citing works
Martin, J. A., Crane‐Droesch, A., Lapite, F. C., Puhl, J., Kmiec, T. E., Silvestri, J. A., Ungar, L., Kinosian, B. P., Himes, B. E., Hubbard, R. A., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab248 · Cited by 17 · see citing works
Lee, D. Y., Kim, N., Park, C., Gan, S., Son, S. J., Park, R. W., & Park, B. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.115817 · Cited by 17 · see citing works
Predicting stroke severity of patients using interpretable machine learning algorithms
Azar, A. S., Samimi, T., Tavassoli, G., Naemi, A., Rahimi, B., Hadianfard, Z., Wiil, U., Nazarbaghi, S., Mohasefi, J. B., & Afshar, H. L. (2024) · European Journal of Medical Research · doi:10.1186/s40001-024-02147-1 · Cited by 15 · see citing works
Self-citationStuhlmiller, T. J., Rabe, A., Rapp, J., Awawda, A., Kouser, H., Lui, K., Salamon, H., Chuyka, D., Mahoney, W., Furgason, J. M., et al. (2025) · medRxiv · doi:10.1101/2025.02.25.25322898 · Cited by 13 · see citing works
Ebrahimi, A., Wiil, U. K., Naemi, A., Mansourvar, M., Andersen, K., & Nielsen, A. S. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02051-w · Cited by 13 · see citing works
Self-citationIdentification of patients with congenital hemophilia in a large electronic health record database
Wang, M., Cyhaniuk, A., Cooper, D. L., & Iyer, N. N. (2017) · Journal of Blood Medicine · doi:10.2147/jbm.s133616 · Cited by 12 · see citing works
Haque, R., Goh, H.-N., Ting, C., Quek, A., & Hasan, M. R. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100356 · Cited by 12 · see citing works
Machine learning tools match physician accuracy in multilingual text annotation
Zielonka, M., Czyżewski, A., Szplit, D., Graff, B., Szyndler, A., Budzisz, M., & Narkiewicz, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-89754-y · Cited by 10 · see citing works
Predicting patients’ sentiments about medications using artificial intelligence techniques
Azar, A. S., Rikan, S. B., Naemi, A., Bagherzadeh, J., & Wiil, U. K. (2024) · Scientific Reports · doi:10.1038/s41598-024-83222-9 · Cited by 8 · see citing works
Self-citationNaemi, A., Tashk, A., Azar, A. S., Samimi, T., Tavassoli, G., Mohasefi, A. B., Khanshan, E. N., Najafabad, M. H., Tarighi, V., Wiil, U. K., et al. (2025) · Cancers · doi:10.3390/cancers17030558 · Cited by 8 · see citing works
Self-citationEliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network
Priya, S. K., & Karthika, K. P. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.032160 · Cited by 8 · see citing works
Azar, A. S., Ghafari, A., Najar, M. O., Rikan, S. B., Ghafari, R., Khamene, M. F., & Sheikhzadeh, P. (2021) · Frontiers in biomedical technologies · doi:10.18502/fbt.v8i2.6517 · Cited by 6 · see citing works
Important steps for artificial intelligence-based risk assessment of older adults
Wiil, U. K. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00155-3 · Cited by 6 · see citing works
Self-citationYuan, H. (2025) · Medicine Advances · doi:10.1002/med4.70002 · Cited by 5 · see citing works
Hunter, J., Nicandri, G. T., & Bozic, K. J. (2024) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003261 · Cited by 4 · see citing works
Nundy, S., Kakar, A., & Bhutta, Z. A. (2021) · Springer eBooks · doi:10.1007/978-981-16-5248-6_45 · Cited by 4 · see citing works
Lázaro, E., & Moscardó, V. (2024) · Healthcare · doi:10.3390/healthcare12192008 · Cited by 3 · see citing works
A Depth Evidence Score Fusion Algorithm for Chinese Medical Intelligence Question Answering System
Zhou, X., Wu, B., & Zhou, Q. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/1205354 · Cited by 3 · see citing works
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Explainability of decoder-only clinical large language models: A scoping review
Mishra, N., Abu-Hanna, A., & Calixto, I. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103501
Rahmaniar, W., Sunarwidhi, A. L., & Hernawan, A. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109280
- Authors
- Sorayaie Azar, A., Bagherzadeh Mohasefi, J., Wiil, U. K., Naemi, A., & Ebrahimi, A.
- Affiliations
- University of Southern Denmark (DK)
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2026-08
- Volume
- 178
- Pages
- 103441
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W7157074496
- Cited by
- 2
- References
- 57
- Citations by year
- 2026: 2
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- clinical text mining, clinical natural language processing, BERT, GPT, disease classification, explainable AI, multimodal AI, clinical data quality, clinical trust in AI
- MeSH terms
- Machine Learning, Large Language Models, Generative Artificial Intelligence, Artificial Intelligence, Humans, Natural Language Processing, Data Mining, Electronic Health Records
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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.
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