The clinicians’ guide to large language models: a general perspective with a focus on hallucinations
Open accessAbstract
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations. Hallucinations (false information) generated by LLMs arise from a multitude of causes, including both factors related to the training dataset as well as their auto-regressive nature. The implications for clinical practice range from the generation of inaccurate diagnostic and therapeutic information to the reinforcement of flawed diagnostic reasoning pathways, as well as a lack of reliability if not used properly. To reduce this risk, we developed a general technical framework for approaching LLMs in general clinical practice, as well as for implementation on a larger institutional scale.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Hallucination, in which LLMs generate confident but factually incorrect outputs, has been documented in clinical contexts with direct implications for patient safety.
“Hallucination, in which LLMs generate confident but factually incorrect outputs, has been documented in clinical contexts with direct implications for patient safety”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Roustan and Bastardot, 2025
Hallucinated content in clinical settings can vary in severity, ranging from minor factual inconsistencies to unsupported treatment recommendations that may directly harm patients.
“hallucinated content can vary in severity, ranging from minor factual inconsistencies to unsupported treatment recommendations that may directly harm patients”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Roustan and Bastardot, 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · cited 2× · see citing works
“First, both the quality and volume of the dataset upon which the LLM has been trained are important variables and can explain the number of hallucinations the LLM produces to some degree [7,8].”
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101 · see citing works
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613 · cited 1× · see citing works
“Within this field, LLM performance has already been evaluated to take specialist board exams [3], improve communication with patients [4,5], and write drafts for scientific papers.”
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738 · see citing works
Detecting hallucinations in large language models using semantic entropy
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature · doi:10.1038/s41586-024-07421-0 · Cited by 934 · cited 1× · see citing works
“Measurement of semantic entropy [22], algorithmic approaches to address root causes of hallucinations [23], and more classical methods such as Retrieval-Augmented Generation [24] are some of the many techniques that have been proposed to identify and reduce hallucinations.”
A survey on multimodal large language models
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024) · National Science Review · doi:10.1093/nsr/nwae403 · Cited by 893 · see citing works
ChatGPT: the future of discharge summaries?
Patel, S. B., & Lam, K. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00021-3 · Cited by 728 · see citing works
Bhayana, R., Krishna, S., & Bleakney, R. R. (2023) · Radiology · doi:10.1148/radiol.230582 · Cited by 460 · see citing works
Analyzing and Mitigating Object Hallucination in Large Vision-Language ModelsPreprint
Zhou, Y.-Y., Cui, C.-H., Yoon, J., Zhang, L.-J., Deng, Z., Finn, C., Bansal, M., & Yao, H. (2023) · International Conference on Learning Representations · doi:10.48550/arxiv.2310.00754 · Cited by 399 · see citing works
Using ChatGPT to write patient clinic letters
Ali, S. R., Dobbs, T. D., Hutchings, H. A., & Whitaker, I. S. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00048-1 · Cited by 375 · see citing works
Ethics of large language models in medicine and medical research
Li, H., Moon, J. T., Purkayastha, S., Celi, L. A., Trivedi, H., & Gichoya, J. W. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00083-3 · Cited by 286 · see citing works
The Internal State of an LLM Knows When It's Lying
Azaria, A., & Mitchell, T. (2023) · arXiv · doi:10.48550/arxiv.2304.13734 · Cited by 179 · see citing works
The Internal State of an LLM Knows When It’s Lying
Azaria, A., & Mitchell, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.68 · Cited by 175 · see citing works
ChatGPT and Ophthalmology: Exploring Its Potential with Discharge Summaries and Operative Notes
Singh, S., Djalilian, A. R., & Ali, M. J. (2023) · Seminars in Ophthalmology · doi:10.1080/08820538.2023.2209166 · Cited by 149 · see citing works
ChatGPT and Bard exhibit spontaneous citation fabrication during psychiatry literature search
McGowan, A., Gui, Y., Dobbs, M., Shuster, S., Cotter, M., Selloni, A., Goodman, M., Srivastava, A., Cecchi, G. A., & Corcoran, C. M. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115334 · Cited by 140 · see citing works
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Dziri, N., Milton, S., Yu, M., Zaiane, O., & Reddy, S. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.387 · Cited by 121 · see citing works
Shea, Y. F., Lee, C. M. Y., Ip, W. C., Luk, D. W. A., & Wong, S. S. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.25000 · Cited by 81 · see citing works
Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Chen, J., Gui, C., Ouyang, R., Gao, A., Chen, S., Chen, G. H., Wang, X., Cai, Z., Ji, K., Wan, X., et al. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.418 · Cited by 79 · see citing works
Do Large Language Models Know What They Don’t Know?
Yin, Z., Sun, Q., Guo, Q., Wu, J., Qiu, X., & Huang, X. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.551 · Cited by 75 · see citing works
Do Large Language Models Know What They Don't Know?
Yin, Z., Sun, Q., Guo, Q., Wu, J., Qiu, X., & Huang, X. (2023) · arXiv · doi:10.48550/arxiv.2305.18153 · Cited by 75
Trapping LLM Hallucinations Using Tagged Context PromptsPreprint
Feldman, P. G., Foulds, J., & Pan, S.-M. (2023) · arXiv.org · doi:10.48550/arxiv.2306.06085 · Cited by 63 · see citing works
Chen, T. C., Kaminski, E., Koduri, L., Singer, A., Singer, J., Couldwell, M., Delashaw, J., Dumont, A., & Wang, A. (2023) · World Neurosurgery · doi:10.1016/j.wneu.2023.08.088 · Cited by 59 · see citing works
ChatGPT in head and neck scientific writing: A precautionary anecdote.
Wu, R. T., & Dang, R. R. (2023) · American Journal of Otolaryngology · doi:10.1016/j.amjoto.2023.103980 · Cited by 39 · see citing works
Trapping LLM Hallucinations Using Tagged Context PromptsPreprint
Feldman, P., Foulds, J. R., & Pan, S. (2023) · arXiv (Cornell University) · doi:10.13016/m20k3c-smqk · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Multimodal Large Language Models in Medical Imaging: Current State and Future Directions
Nam, Y., Kim, D., Kyung, S., Seo, J., Song, J. M., Kwon, J., Kim, J., Jo, W., Park, H. J., Sung, J., et al. (2025) · Korean Journal of Radiology · doi:10.3348/kjr.2025.0599 · Cited by 106 · see citing works
Yang, H., Li, M., Zhou, H., Xiao, Y., Fang, Q., Zhou, S., & Zhang, R. (2025) · Journal of Medical Internet Research · doi:10.2196/70080 · Cited by 64 · see citing works
Đerić, E., Frank, D., & Milković, M. (2025) · Information · doi:10.3390/info16070622 · Cited by 52 · see citing works
AI Agents in Clinical Medicine: A Systematic ReviewPreprint
Gorenshtein, A., Omar, M., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2025) · medRxiv · doi:10.1101/2025.08.22.25334232 · Cited by 59 · see citing works
Haider, S. A., Prabha, S., Gomez-Cabello, C. A., Borna, S., Genovese, A., Trabilsy, M., Collaço, B. G., Wood, N. G., Bagaria, S. P., Tao, C., et al. (2025) · Sensors · doi:10.3390/s25144305 · Cited by 19 · see citing works
Camlet, A., Kusiak, A., Ossowska, A., & Świetlik, D. (2025) · Diagnostics · doi:10.3390/diagnostics15151851 · Cited by 16 · see citing works
Artificial intelligence in primary care: frameworks, challenges, and guardrails
Allen, L. N., Lin, J., Segal, B., Ndlovu, K., Bilardi, D., & Pettigrew, L. M. (2025) · The Lancet Primary Care · doi:10.1016/j.lanprc.2025.100079 · Cited by 19 · see citing works
Wang, S., Zhao, F., Bu, D., Lu, Y., Gong, M., Liu, H., Yang, Z., Zeng, X., Yuan, Z., Wan, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64142-2 · Cited by 13 · see citing works
Ghosh, S., & Mittal, G. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1697169 · Cited by 13 · see citing works
Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions
Rahman, L. A., Dedousis, V., Papathanail, I., Poursoleymani, R., Kafyra, M., Kalafati, I. P., & Mougiakakou, S. G. (2026) · Nutrients · doi:10.3390/nu18060938 · Cited by 14 · see citing works
Roeschl, T., Hoffmann, M., Hashemi, D., Rarreck, F., Hinrichs, N., Trippel, T. D., Gröschel, M. I., Unbehaun, A., Klein, C., Kempfert, J., et al. (2025) · JMIRx Med · doi:10.2196/74899 · Cited by 13 · see citing works
Aslan, İ. Ö., & Aslan, M. (2025) · Healthcare · doi:10.3390/healthcare13141756 · Cited by 11 · see citing works
Advanced AI-based technologies for early detection and personalized management of cervical cancer
Zhang, Y., Xu, Y., Wang, C., Zhang, Z., Zhou, K., Zhu, Y., & Yu, X. (2025) · Biomedical Technology · doi:10.1016/j.bmt.2025.100100 · Cited by 11 · see citing works
Gün, M. (2025) · BMC Emergency Medicine · doi:10.1186/s12873-025-01303-y · Cited by 8 · see citing works
Scientific writing in the age of artificial intelligence: trust on trial?
Vaishya, R., Misra, A., & Vaish, A. (2025) · Postgraduate Medical Journal · doi:10.1093/postmj/qgaf215 · Cited by 7 · see citing works
Sirago, G., Calò, F., Vinci, A., Visci, P., Solarino, B., Dell'Erba, A. S., & Ferorelli, D. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02363-8 · Cited by 9 · see citing works
Artificial Intelligence Large Language Models in Cardiology
Chen, J. M., Liang, Y., & Ge, J. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm39452 · Cited by 6 · see citing works
Vasilev, Y., Raznitsyna, I., Pamova, A., Burtsev, T., Bobrovskaya, T., Kosov, P., Vladzymyrskyy, A., Omelyanskaya, O., & Arzamasov, K. (2025) · Diagnostics · doi:10.3390/diagnostics16010003 · Cited by 5 · see citing works
DAX Copilot: ambient AI scribe may help reduce surgical resident clinical documentation burden
Ghanem, Y. K., Nation, R. J., Sofield, H., Rouhi, A. D., Gandhi, S., Sier, R., Saleh, Z., Joshi, A. R. T., & Patel, R. (2025) · Surgical Endoscopy · doi:10.1007/s00464-025-12404-x · Cited by 5 · see citing works
Building Trustworthy AI in Healthcare
Amadi, C., & Ojo, A. (2025) · IEEE Access · doi:10.1109/access.2025.3648410 · Cited by 9 · see citing works
Algorithmic sycophancy: A new source of systematic distortion in AI-driven biomedical research
Sharma, H., & Ruikar, M. (2026) · Journal of Postgraduate Medicine · doi:10.4103/jpgm.jpgm_919_25 · Cited by 6 · see citing works
De la Torre-Trillo, J., Yáñez Castillo, Y., Melgarejo Segura, M. T., Carmona Sánchez, E., Zambudio Munuera, A., Mora-Delgado, J., & López Luque, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15062130 · Cited by 5 · see citing works
Generative AI as a de facto mental health provider: a policy brief and urgent call for regulation
Refoua, E., Gigi, K., Levkovich, I., Shoval, D. H., Elyoseph, Z., Tsafrir, I., Pen, O., Angert, T., & Haber, Y. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1708806 · Cited by 5 · see citing works
Kim, S. I., Park, J., Kim, T., Seo, W., Kim, T., Cha, W. C., & Hong, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-31113-y · Cited by 5 · see citing works
Gün, M., & Aktaş, Y. (2025) · American Journal of Hospice and Palliative Medicine® · doi:10.1177/10499091251352100 · Cited by 5 · see citing works
Neuro-symbolic LLM Integration in Clinical Medicine: A Systematic Review and TaxonomyPreprint
Gorenshtein, A., Omar, M., Barash, Y., Nadkarni, G. N., & Klang, E. (2026) · Research Square · doi:10.21203/rs.3.rs-8902022/v1 · Cited by 3 · see citing works
Dymm, B., & Khalid, A. (2025) · Seminars in Neurology · doi:10.1055/a-2713-6622 · Cited by 4 · see citing works
Elsharif, W., Alzubaidi, M. S., Tukur, M., Magram, A., Anver, F., Hamza, A., Said, S. A., Khan, R., Househ, M., & Agus, M. (2025) · BioMedical Engineering OnLine · doi:10.1186/s12938-025-01486-2 · Cited by 5 · see citing works
Lim, J., Tan, P., Gutai, F., Sainsbury, D. C. G., Strong, B., Varma, S., & Lewis, C. (2026) · Journal of Plastic Reconstructive & Aesthetic Surgery · doi:10.1016/j.bjps.2026.01.032 · Cited by 4 · see citing works
Tezcan, H., Tunçez, A., Gürses, K. M., Özen, Y., & Yalçın, M. U. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09141-7 · Cited by 4 · see citing works
Rice, J., Ó'Briain, E., Kilkenny, C. J., Hogan, R. E., McIntyre, T. V., Kavanagh, D. O., Neary, P. C., O’Riordan, J. M., & Sahebally, S. M. (2025) · Journal of surgical education · doi:10.1016/j.jsurg.2025.103676 · Cited by 3 · see citing works
Martin, G. L., & Létinier, L. (2025) · Therapies · doi:10.1016/j.therap.2025.10.001 · Cited by 3 · see citing works
Evaluation of large language models in assigning PI-RADS v2.1 categories for prostate MRI reports
Dölek, B. A., & Beşler, M. S. (2026) · BMC Urology · doi:10.1186/s12894-025-02038-5 · Cited by 3 · see citing works
The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa
Stewart, J. W., Barth, B. A., & Rojas, I. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1739000 · Cited by 3 · see citing works
Jung, J., Kim, H., Bae, S., & Park, J. Y. (2026) · Medicine · doi:10.1097/md.0000000000047071 · Cited by 3 · see citing works
Ekingen, E., & Üçdal, M. T. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15051730 · Cited by 3 · see citing works
Christodoulou, R. C., Papageorgiou, P. S., Pereira, A. C. L., Solomou, E. E., Papageorgiou, S. G., Vassiliou, E. K., & Georgiou, M. F. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1795441 · Cited by 3 · see citing works
The AI interviewer: multi-faceted evaluation of adaptive questioning by large language models
Panfilova, A. S., Bolshev, V., Mozikov, M., Латынов, В. В., Vanin, A. V., Nestik, T. A., Nourkova, V. V., Александровна, В. А., Kozin, M., Serohvostov, A., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-46517-7 · Cited by 3 · see citing works
Artificial Intelligence in Medicine: Basic Knowledge, Applications, Ethics and Perspectives
Cascella, M., Avella, M. T., Pumpo, M. D., Sebastiani, E., Bussa, M., Tagliaferri, A., Brunetti, B., Meloni, P., Bianchini, E., Graziani, P., et al. (2026) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70484 · Cited by 5 · see citing works
Greydanus, D. E., Azeem, M. W., & Nazeer, A. (2025) · Disease-a-Month · doi:10.1016/j.disamonth.2025.101983 · Cited by 3 · see citing works
Jiang, W., Wang, D., Zeng, Y., Huang, J., Xu, C., & Liu, C. (2025) · Journal of Medical Internet Research · doi:10.2196/80770 · Cited by 2 · see citing works
Petrovic, M. G., Babić, S., Dimitrijevic, A. M., Lozuk, B., & Bojic, M. (2025) · Srpski arhiv za celokupno lekarstvo · doi:10.2298/sarh240704053p · Cited by 2 · see citing works
DiGirolamo, R. (2025) · Journal of Applied Animal Welfare Science · doi:10.1080/10888705.2025.2606681 · Cited by 2 · see citing works
Governance for Generative AI in Clinical Development: A Cross‑Sectional Survey in Japan
Takata, M., Sugimoto, M., Harada, M., Hiraide, M., Ichikawa, D., Hayashi, Y., Fukimbara, S., Kawaguchi, T., Suganami, H., & Yamaguchi, T. (2026) · Journal of the Society for Clinical Data Management · doi:10.47912/jscdm.486 · Cited by 2 · see citing works
Recent Progress in Deep Learning for Chest X-Ray Report Generation
Salhi, M., & Akhloufi, M. A. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6010003 · Cited by 2 · see citing works
Putra, P. K., Nurfadhilah, E., Pebiana, S., & Prakasa, E. (2025) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina68387.2025.11325125 · Cited by 2 · see citing works
Evaluation of algorithmic bias in large language models for retinal clinical recommendations
Gandhi, S., Balas, M., Popovic, M. M., Kertes, P. J., Muni, R. H., & Kohly, R. P. (2026) · AJO International · doi:10.1016/j.ajoint.2026.100233 · Cited by 2 · see citing works
Nartthanarung, A., Plangsiri, K., & Kongmalai, P. (2026) · Advances in Medical Education and Practice · doi:10.2147/amep.s603679 · Cited by 4 · see citing works
Kamani, M. O. (2026) · International Urogynecology Journal · doi:10.1007/s00192-026-06660-1 · Cited by 2 · see citing works
Wang, S. Q. G., Mou, K., Wang, J., Mo, C., Shen, X., Wang, J., Zuo, W., & Chen, Y. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09223-6 · Cited by 2 · see citing works
Warren, T., van der Weegen, W., Kool, R. B., Hoogendoorn, M., & Timmers, T. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106525 · Cited by 2 · see citing works
Gibson, B. N., Chodapuneedi, S., Koh, H. Y., Gupta, S., Gašević, D., Wang, A. Y., Marzolini, C., Khoo, S., Romero, L., Dalli, L. L., et al. (2026) · British Journal of Pharmacology · doi:10.1111/bph.70515 · Cited by 2 · see citing works
Ethical Considerations in Personal Health Large Language Models
Liu, J., & Liu, S. (2026) · Journal of Medical Internet Research · doi:10.2196/92240 · Cited by 2 · see citing works
Saleem, N. H., Amaranathan, J., Dandoush, A., Rye, S., King, P. T., & Chin, M. H. (2026) · Medical Teacher · doi:10.1080/0142159x.2026.2691072 · Cited by 3 · see citing works
Generative AI and Large Language Models in Rehabilitation: A Scoping Review
Cha, S. (2026) · Life · doi:10.3390/life16081310 · Cited by 2 · see citing works
Cho, A.-Y., Eo, S., Jeon, M., Kim, J., Ihm, J., & Seo, D. (2026) · JMIR Medical Education · doi:10.2196/91809 · Cited by 2 · see citing works
Development of a RAG-based Expert LLM for Clinical Support in Radiation OncologyPreprint
Liu, T., Wang, X., Inkman, M. J., Hong, J. C., Waters, M. R., & Zhang, J. (2025) · medRxiv · doi:10.1101/2025.09.16.25335813 · Cited by 3 · see citing works
Tonkaz, D. E., Tonkaz, M., Bekçi, T., & Tonkaz, G. (2025) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2025.10.002 · Cited by 1 · see citing works
Hao, P., Zhang, J., Zhang, S., Wu, F., & Tian, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-41439-w · Cited by 1 · see citing works
Forero, D. A. (2026) · Current Genomics · doi:10.2174/0113892029403403260112075225 · Cited by 1 · see citing works
Seripenah, P., Ikechukwu, P., Oni, G., Polotto, S., Adeboye, W., Leonardi‐Bee, J., Jordan, C., Morling, J., Aiyelabegan, F., Dhungana, S., et al. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23050545 · Cited by 1 · see citing works
Palmer, K. M., Shalom, R. O., Dingle, J., Bresler, T. E., Meyer, R. J., SAHLOOL, Y., & Carroll, N. (2026) · Journal of Cardiac Failure - Intersections · doi:10.1016/j.yjcafi.2026.03.008 · Cited by 1 · see citing works
Tekdemir, H., Çıvgın, E., Akpınar, Ş., Ekici, O., Tekdemir, B. N., Özdemir, A. B., Altaş, E., Toker, A. S., Parlak, E. Ş., & Parlak, S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01989-x · Cited by 1 · see citing works
Wang, C. G., Bosson, N., Rahimian, R., Schlesinger, S. A., Whitfield, D. A., & Toy, J. (2026) · Prehospital Emergency Care · doi:10.1080/10903127.2026.2668008 · Cited by 1 · see citing works
Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review
Rai, M. C. E., Far, A. B., Darweesh, M., Dhou, S., Aburaed, N., El, S., Elkhazendar, M., & Ellahham, S. (2026) · Diagnostics · doi:10.3390/diagnostics16101485 · Cited by 1 · see citing works
Molu, B. (2026) · Journal of Paediatrics and Child Health · doi:10.1111/jpc.70431 · Cited by 1 · see citing works
Gandhi, S., & Balas, M. (2026) · BMC Medical Ethics · doi:10.1186/s12910-026-01496-2 · Cited by 1 · see citing works
Sriram, N., Jain, R., Mittal, M., Patel, R. B., Fredeen, B. J., Bajaj, A., Du, R., Habashy, K. J., Kemeny, H. R., Govind, S., et al. (2026) · Healthcare · doi:10.3390/healthcare14121640 · Cited by 1 · see citing works
AI in respiratory care: findings from the GOLD report
Al-waqeerah, A., Bashah, A., Ghaleb, E., He, H., & Gao, L. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08454-x · Cited by 1 · see citing works
Application of large language models in medical diagnosis: A bibliometric review
Zhang, Q., Wang, H., Li, H., Jiao, F., Zhou, H., & Li, M. (2026) · Digital Health · doi:10.1177/20552076261462624 · Cited by 1 · see citing works
Jeong, I. H., Kim, H., & Hwang, H. (2026) · Kosin Medical Journal · doi:10.7180/kmj.26.173 · Cited by 1 · see citing works
Medical question answering: A comprehensive multimodal and LLM-driven survey
Mhedhbi, E., Zhu, X., Ayaz, M., Khan, M., & Othmani, A. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109541 · Cited by 1 · see citing works
Artificial Intelligence in Wilderness Search and Rescue: A Narrative Review
Iserson, K. V. (2026) · Wilderness and Environmental Medicine · doi:10.1177/10806032261465600 · Cited by 1 · see citing works
Li, Q., Sun, Y., Ju, X., & Cui, J. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106603 · Cited by 1 · see citing works
Che, X., & Liu, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1910543 · Cited by 2 · see citing works
Zhou, J., Zhou, Q., Zhou, L., Wang, J., Gao, J., Qin, L., Tan, B., Fan, P., & Li, C. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1921819 · Cited by 1 · see citing works
Şahi̇n, M., Angelopoulos, N., & Paparodis, R. D. (2026) · Endocrine Connections · doi:10.1530/ec-26-0415 · Cited by 1 · see citing works
Development of a hybrid artificial intelligence framework for pharmacotherapy optimization
Rose, O., Clemens, S., Leiherer, A., Bücker, M., Petersson, F., Lirk, G., Mosch, C., Pachmayr, J., & Hoti, K. (2026) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-026-02205-0 · Cited by 1 · see citing works
Jung, J., Kim, H., Bae, S., & Park, J. Y. (2025) · medRxiv · doi:10.1101/2025.06.22.25330068 · Cited by 1 · see citing works
McLaughlin, L., Walz, M. S., & Arries, C. (2026) · medRxiv · doi:10.64898/2026.02.18.26346559 · Cited by 2 · see citing works
Naser, M. Z. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09938-4 · Cited by 2 · see citing works
Barki, C., Chouchen, M., Sediri, A., Ceylan, H. İ., Rahmouni, H. B., Muntean, R. I., El-Seedi, H. R., Bragazzi, N. L., & Dergaa, I. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1892418 · Cited by 1 · see citing works
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
Huang, Y., Chen, R., & Cheng, J. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.917
Meng, Y., Liu, Z., Zhang, Y., Liu, B., Chen, J., Qu, Z., Li, Z., Lei, Z., & Hu, Z. (2025) · doi:10.2196/preprints.82469
An Overview of Artificial Intelligence in Neurology
Parker, T., & Brush, B. (2025) · Seminars in Neurology · doi:10.1055/a-2693-0547
Silva, A. P. D., & Prabagar, K. (2025) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v6.i2.110109
Tsybulnik, D. Y., Gillette, J. J., & Heston, T. F. (2025) · medRxiv · doi:10.1101/2025.10.03.25337291
Kashlan, R., Duggireddy, H., Smith, J. J., Faraj, R. R., Gattas, S., Grossberg, J. A., & Gichoya, J. W. (2025) · Research Square · doi:10.21203/rs.3.rs-7744596/v1
Ardila, C. M. (2025) · Journal of Prosthodontics · doi:10.1111/jopr.70072
A Comparative Performance Study of Retrieval-Augmented Generation Systems in Gynecologic OncologyPreprint
Peng, S., Kuerbanjiang, W., Xiao, H., He, X., & Yi, Y. (2025) · Research Square · doi:10.21203/rs.3.rs-8221196/v1
Fukataki, Y., Hayashi, W., Kitayama, M., & Ito, Y. M. (2026) · medRxiv · doi:10.64898/2026.01.01.26343326
Kabakuş, A. T. (2026) · Gazi University Journal of Science Part A Engineering and Innovation · doi:10.54287/gujsa.1819131
Wang, J., Cao, Z., Tian, Z., Sun, B., & Cheng, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-38006-8
Kudaş, İ. (2026) · Journal of Surgery and Medicine · doi:10.28982/josam.8536
Dey, S. K., Manvi, S., Thapa, A., Shah, M. K., Mehta, Z., Kapile, S. K., Divate, T., Jalota, S., & Ismail, A. (2026) · Journal of Medical Internet Research · doi:10.2196/86545
Nag, A., & Chaudhuri, D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7534-2.ch012
Şalbaş, A. (2026) · Medical Records · doi:10.37990/medr.1739579
Farhad, M., Masud, M. M., Rasheed, A. S. A., & Musthfa, N. (2026) · doi:10.1145/3793638.3793643
Liu, Z., Yang, X., Zhang, Y., Xu, Y., Xiang, Y., & Wang, H. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1770564
Russ, P. G., Bedenbender, S., Einloft, J., Meyer, H. L., Wenzel, L. T., Ganser, A., Hirsch, M. C., & Grgic, I. (2026) · Scientific Reports · doi:10.1038/s41598-026-46846-7 · Cited by 1 · see citing works
- Authors
- Roustan, D., & Bastardot, F.
- Affiliations
- Cliniques Universitaires Saint-Luc (BE)
- Centre Hospitalier Universitaire Vaudois (CH)
- Corresponding author
- Roustan, Dimitri
- Venue
- Interactive Journal of Medical Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-01-28
- Volume
- 14
- Pages
- e59823
- ISSN
- 1929-073X (ISSN-L)
- PMC
- PMC11815294
- OpenAlex
- W4406887664
- Cited by
- 191
- References
- 23
- Citations by year
- 2026: 150 · 2025: 40 · 2024: 1
- FWCI
- 45.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Interactive Journal of Medical Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IRIS (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- large language models, hallucinations, clinical practice, diagnostic reasoning, training data quality, autoregressive generation
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 ›
Are you an author, or do you see a problem with this page? Request a review
