The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives
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Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.
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Multimodal LLMs for retinal disease diagnosis via OCT: few-shot versus single-shot learning
Agbareia, R., Omar, M., Zloto, O., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2025) · Therapeutic Advances in Ophthalmology · doi:10.1177/25158414251340569 · Cited by 16 · see citing works
Assessing the Impact of New Technologies on Managing Chronic Respiratory Diseases
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Open E-survey on the Use and Perception of Chatbots in Vascular Surgery
Lareyre, F., D’Oria, M., Caradu, C., Jongkind, V., Lorenzo, G. D., Smeds, M. R., Nasr, B., Raffort, J., Enzmann, F., de Borst, G. J., et al. (2024) · EJVES Vascular Forum · doi:10.1016/j.ejvsvf.2024.07.037 · Cited by 15 · see citing works
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Identifying healthcare needs with patient experience reviews using ChatGPT
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Shi, B., Chen, L., Pang, S., Wang, Y., Wang, S., Li, F., Zhao, W., Guo, P., Zhang, L., Fan, C., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/67253 · Cited by 15 · see citing works
The Role of Prompt Engineering for Multimodal LLM Glaucoma DiagnosisPreprint
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Reimagining patient-reported outcomes in the age of generative AI
Boyer, L., Fernandes, S., Auquier, P., Falissard, B., & Panch, T. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02006-1 · Cited by 14 · see citing works
Bentegeac, R., Florens, N., Maanaoui, M., Maisons, V., Lanot, A., Bobot, M., Brilland, B., Glowacki, F., Gerard, E., Hazzan, M., et al. (2025) · Clinical Kidney Journal · doi:10.1093/ckj/sfaf308 · Cited by 13 · see citing works
Performance of artificial intelligence on a simulated Canadian urology board exam
Touma, N. J., Caterini, J. E., & Liblk, K. (2024) · Canadian Urological Association Journal · doi:10.5489/cuaj.8800 · Cited by 13 · see citing works
Large language models and artificial intelligence chatbots in vascular surgery
Lareyre, F., Nasr, B., Poggi, E., Lorenzo, G. D., Ballaith, A., Sliti, I., Chaudhuri, A., & Raffort, J. (2024) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2024.06.001 · Cited by 14 · see citing works
A Review of Applying Large Language Models in Healthcare
Liu, Q., Yang, R., Gao, Q. F., Liang, T., Wang, X., Li, S., Lei, B., & Gao, K. (2024) · IEEE Access · doi:10.1109/access.2024.3524588 · Cited by 15 · see citing works
Araujo, M. L. D., Winger, T., Ghosn, S., Saab, C. Y., Srivastava, J., Kazaglis, L., Mathur, P., & Mehra, R. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.04.033 · Cited by 16 · see citing works
Zhang, Z., Nezhad, M. J. M., Hosseini, S. M. B., Zolnour, A., Zonour, Z., Hosseini, S. M., Topaz, M., & Zolnoori, M. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106231 · Cited by 14 · see citing works
Alibudbud, R. C., Aruta, J. J. B. R., Sison, K. A., & Guinto, R. (2024) · International Review of Psychiatry · doi:10.1080/09540261.2024.2363373 · Cited by 12 · see citing works
Supercharge Your Academic Productivity with Generative Artificial Intelligence
Lonsdale, H., O’Reilly-Shah, V. N., Padiyath, A., & Allan F. Simpao, M. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02093-9 · Cited by 13 · see citing works
Gill, G. S., Blair, J., & Litinsky, S. M. (2024) · Cureus · doi:10.7759/cureus.73812 · Cited by 11 · see citing works
Large language models for improving cancer diagnosis and management in primary health care settings
Andrew, A., & Tizzard, E. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100157 · Cited by 11 · see citing works
Güneş, Y. C., Cesur, T., Çamur, E., Çifçi, B. E., Kaya, T., Colakoglu, M. N., Koç, U., & Ökten, R. S. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.01.004 · Cited by 11 · see citing works
Güvel, M. C., Kıyak, Y. S., Varan, H. D., Sezenöz, B., Çoşkun, Ö., & Uluoğlu, C. (2025) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-025-03838-2 · Cited by 12 · see citing works
Artiaga, J. C. M., Guevarra, M. C. B., Sosuan, G. M. N., Agnihotri, A. P., Nagel, I. D., & Kalaw, F. G. P. (2025) · Eye · doi:10.1038/s41433-025-03935-7 · Cited by 11 · see citing works
Mondal, H., Dash, I., Mondal, S., Varikasuvu, S. R., Gayen, R. K., Sharma, S., & Biri, S. K. (2025) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2025.2568090 · Cited by 10 · see citing works
Temporal evolution of large language models (LLMs) in oncology
Qiu, Z., Jiang, A., Chang, Q., Gan, W., Zhu, L., Mou, W., Zeng, D., Xiao, M., Chu, G., Peng, S., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07227-2 · Cited by 10 · see citing works
Bianco, G. L., Robinson, C. L., D’Angelo, F. P., Cascella, M., Natoli, S., Sinagra, E., Mercadante, S., & Drago, F. (2025) · Biomedicines · doi:10.3390/biomedicines13030636 · Cited by 10 · see citing works
Self-citationThe Role of Claude 3.5 Sonet and ChatGPT-4 in Posterior Cervical Fusion Patient Guidance
Nasirov, R. (2025) · World Neurosurgery · doi:10.1016/j.wneu.2025.123889 · Cited by 10 · see citing works
Kakaday, R., Herrera, E., Pipitone, O., Hertel, A. W., & Kaiser, P. (2025) · Applied Clinical Informatics · doi:10.1055/a-2559-5791 · Cited by 10 · see citing works
Lim, B., Seth, I., Maxwell, M., Cuomo, R., Ross, R. J., & Rozen, W. M. (2025) · Aesthetic Plastic Surgery · doi:10.1007/s00266-025-04842-8 · Cited by 10 · see citing works
Battle of the Bots: Solving Clinical Cases in Osteoarticular Infections With Large Language Models
Borgonovo, F., Matsuo, T., Petri, F., Amin Alavi, S. M., Mazudie Ndjonko, L. C., Gori, A., & Berbari, E. F. (2025) · Mayo Clinic Proceedings: Digital Health · doi:10.1016/j.mcpdig.2025.100230 · Cited by 10 · see citing works
Advancing medical question answering with a knowledge embedding transformer
Zhu, X., Khan, M., Ahmed, A. T., & Othmani, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0329606 · Cited by 10 · see citing works
Sprint, G., Schmitter‐Edgecombe, M., & Cook, D. J. (2024) · JMIR Formative Research · doi:10.2196/63866 · Cited by 9 · see citing works
Application of large language models in healthcare: A bibliometric analysis
Zhang, L., Zhao, Q., Zhang, D., Song, M., Zhang, Y., & Wang, X. (2025) · Digital Health · doi:10.1177/20552076251324444 · Cited by 9 · see citing works
Enhancing Policy Generation with GraphRAG and YouTube Data: A Logistics Case Study
Naganawa, H., & Hirata, E. (2025) · Electronics · doi:10.3390/electronics14071241 · Cited by 9 · see citing works
Ilić, N., Marić, N., Cvetković, D., Bogosavljević, M., Bukara-Radujković, G., Krstić, J., Paunović, Z., Begović, N. N., Zarić, S. P., Todorovic, S., et al. (2025) · Genes · doi:10.3390/genes16070762 · Cited by 10 · see citing works
Wang, Y., Wan, Y., Lei, X., Chen, Q., & Hu, H. (2025) · Array · doi:10.1016/j.array.2025.100504 · Cited by 12 · see citing works
The Role of Large Language Model Chatbots in Sexual Education: An Unmet Need of Research
Mondal, H., & Mondal, S. (2025) · Journal of Psychosexual Health · doi:10.1177/26318318251323714 · Cited by 10 · see citing works
Sezer, B., & Okutan, A. E. (2025) · BMC Oral Health · doi:10.1186/s12903-025-06791-9 · Cited by 8 · see citing works
Jayasrilakshmi, S., & Mahapatra, A. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111469 · Cited by 12 · see citing works
Rajakumar, H. K., Sankaran, K. A., Ashok, M. P., & Rachoori, S. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag005 · Cited by 8 · see citing works
Karataş, G., & Karataş, M. E. (2026) · Strabismus · doi:10.1080/09273972.2025.2536782 · Cited by 7 · see citing works
Robot guardians: Mitigating tourists' deviant behavior with intelligent robots
Zhang, M., Pang, S., Liu, N., Shi, S., & Li, X. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105284 · Cited by 7 · see citing works
Sökmen, T., Kar, İ., Tuncer, C., & Tuncer, B. B. (2025) · BMC Oral Health · doi:10.1186/s12903-025-07153-1 · Cited by 7 · see citing works
Geduk, G., Hasırcı, U. C., DUMANLI, D., Aras, R. Ç., Çapar, İ., Altın, E., & Şeker, Ç. (2026) · Scientific Reports · doi:10.1038/s41598-026-37800-8 · Cited by 7 · see citing works
Socio-demographic gaps in pain management guided by large language models
Omar, M., Soffer, S., Agbareia, R., Bragazzi, N. L., Glicksberg, B. S., Hurd, Y. L., Apakama, D. U., Charney, A. W., Reich, D. L., Nadkarni, G. N., et al. (2026) · Nature Health · doi:10.1038/s44360-025-00017-6 · Cited by 7 · see citing works
The Rise of Large Language Models in the Medical Field: A Bibliometric Analysis
Qi, W., Cao, S., Wang, B., Zhu, X., Dong, C., He, D., Chen, Y., Shi, Y., & Wang, B. (2024) · doi:10.1109/fmsys62467.2024.00014 · Cited by 7 · see citing works
Enhancing Chinese Medical Diagnostic Chatbot through Supervised Fine-Tuning of Large Language Models
Ihnaini, B., Huang, Y., Li, L., Wei, J., & Qi, S. (2024) · International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI) · doi:10.1109/iotaai62601.2024.10692873 · Cited by 7 · see citing works
Perogamvros, L., Rochas, V., Beau, J.-B., Virginie Sterpenich, A., & Bayer, L. (2025) · Journal of Sleep Research · doi:10.1111/jsr.70001 · Cited by 6 · see citing works
Reclaiming Patient-Centered Care: How Intelligent Time is Redefining Healthcare Priorities
Bignami, E. G., Russo, M., & Bellini, V. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02163-6 · Cited by 6 · see citing works
Self-citationSuenghataiphorn, T., Danpanichkul, P., Tribuddharat, N., & Kulthamrongsri, N. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.102627 · Cited by 6 · see citing works
Evaluating the Quality of Cardiovascular Disease Information From AI Chatbots: A Comparative Study
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Evaluating Large Language Models for Radiology Systematic Review Title and Abstract Screening
Dogra, S., Arabshahi, S., Wei, J., Hu, E., Saidenberg, L., Sharma, S., Gu, Z., Siriruchatanon, M., & Kang, S. K. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.08.014 · Cited by 5 · see citing works
Large language models in obesity: a systematic review
Suenghataiphorn, T., Tribuddharat, N., Danpanichkul, P., & Kulthamrongsri, N. (2025) · International Journal of Obesity · doi:10.1038/s41366-025-01992-2 · Cited by 6 · see citing works
Medical QA dialogue datasets in RAG systems performance evaluation and ChatGPT optimization
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Large Language Models for Healthcare: Applications, Models, Datasets, and Challenges
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Fine-Tuning Large Language Models for Network Traffic Analysis in Cyber Security
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The Applications of AI Tools in the Fields of Weather and Climate—Selected Examples
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Large Language Models in Spine Surgery: A Promising Technology
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Multimodal LLMs for Retinal Disease Diagnosis via OCT: Few-Shot vs Single-Shot LearningPreprint
Agbareia, R., Omar, M., Zloto, O., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2024) · medRxiv · doi:10.1101/2024.11.02.24316624 · Cited by 5 · see citing works
Araújo, M., Winger, T., Ghosn, S., Saab, C. Y., Srivastava, J., Kazaglis, L., Mathur, P., & Mehra, R. (2025) · medRxiv · doi:10.1101/2025.02.27.25322950 · Cited by 5 · see citing works
- Authors
- Cascella, M., Semeraro, F., Montomoli, J., Bellini, V., Piazza, O., & Bignami, E.
- Affiliations
- University of Salerno (IT)
- Ospedale Maggiore Carlo Alberto Pizzardi (IT)
- Ospedale Infermi di Rimini (IT)
- University of Parma (IT)
- Corresponding author
- Bellini, Valentina
- ORCID
- Venue
- Journal of Medical Systems
- Published
- 2024-02-17
- Volume
- 48
- Issue
- 1
- Pages
- 22-22
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- PMC
- PMC10873461
- OpenAlex
- W4392782765
- Cited by
- 219
- References
- 40
- Citations by year
- 2026: 65 · 2025: 103 · 2024: 51
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Medical Systems (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Università degli Studi di Parma
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, medical applications of AI, clinical decision support, healthcare chatbots, triage, multimodal AI, chronic disease management
- MeSH terms
- Communication, Documentation, Educational Status, Humans, Language, Electric Power Supplies
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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