Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment

Receiver Operating Characteristic Curve in Diagnostic Test Assessment

Mandrekar, J. N. (2010) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3181ec173d · Cited by 4,506 · see citing works

Large Language Models Encode Clinical Knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083 · see citing works

Large language models encode clinical knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works

Large language models in medicine

Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works

Calibration: the Achilles heel of predictive analytics

On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019) · BMC Medicine · doi:10.1186/s12916-019-1466-7 · Cited by 2,650 · see citing works

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical ExamsPreprint

Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., & Szolovits, P. (2020) · Applied Sciences · doi:10.48550/arxiv.2009.13081 · Cited by 2,320 · see citing works

Toward expert-level medical question answering with large language models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734 · see citing works

Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature

Alba, A. C., Agoritsas, T., Walsh, M., Hanna, S., Iorio, A., Devereaux, P. J., McGinn, T., & Guyatt, G. (2017) · JAMA · doi:10.1001/jama.2017.12126 · Cited by 1,631 · see citing works

Second opinion needed: Communicating uncertainty in medical machine learning

Kompa, B., Snoek, J., & Beam, A. L. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00367-3 · Cited by 514 · see citing works

Towards Expert-Level Medical Question Answering with Large Language Models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.09617 · Cited by 338 · see citing works

Towards Accurate Differential Diagnosis with Large Language Models

McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.00164 · Cited by 311 · see citing works

Towards accurate differential diagnosis with large language models

McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., et al. (2025) · Nature · doi:10.1038/s41586-025-08869-4 · Cited by 311 · see citing works

Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine

Savage, T., Nayak, A., Gallo, R., Rangan, E., & Chen, J. H. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01010-1 · Cited by 265 · see citing works

Chatbot vs medical student performance on free-response clinical reasoning examinations.

Strong, E., DiGiammarino, A., Weng, Y., Kumar, A., Hosamani, P., Hom, J., & Chen, J. H. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.2909 · Cited by 169 · see citing works

Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language ModelsPreprint

Huang, Y., Song, J.-Y., Wang, Z., Chen, H.-M., & Ma, L. (2023) · arXiv.org · doi:10.48550/arxiv.2307.10236 · Cited by 144 · see citing works

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.18416 · Cited by 117 · see citing works

Tackling prediction uncertainty in machine learning for healthcare

Chua, M., Kim, D., Choi, J., Lee, N. G., Deshpande, V., Schwab, J., Lev, M. H., Gonzalez, R. G., Gee, M. S., & Do, S. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00988-x · Cited by 114 · see citing works

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

Walsh, C. G., Sharman, K., & Hripcsak, G. M. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.10.008 · Cited by 102 · see citing works

Use of GPT-4 to Analyze Medical Records of Patients With Extensive Investigations and Delayed Diagnosis

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

Large language models will not replace healthcare professionals: curbing popular fears and hype.

Thirunavukarasu, A. J. (2023) · Journal of the Royal Society of Medicine · doi:10.1177/01410768231173123 · Cited by 63 · see citing works

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Kuhn, L., Gal, Y., & Farquhar, S. (2023) · arXiv · doi:10.48550/arxiv.2302.09664 · Cited by 52 · see citing works

Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Xiong, M., Hu, Z., Lu, X., Li, Y., Fu, J., He, J., & Hooi, B. (2023) · arXiv · doi:10.48550/arxiv.2306.13063 · Cited by 52 · see citing works

Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models

Zhou, K., Jurafsky, D., & Hashimoto, T. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.335 · Cited by 38 · see citing works

SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Manakul, P., Liusie, A., & Gales, M. J. F. (2023) · arXiv · doi:10.48550/arxiv.2303.08896 · Cited by 38 · see citing works

When to Retrieve: Teaching LLMs to Utilize Information Retrieval EffectivelyPreprint

Labruna, T., Campos, J. A., & Azkune, G. (2024) · Recent Advances in Natural Language Processing · doi:10.48550/arxiv.2404.19705 · Cited by 29 · see citing works

Benchmarking LLMs via Uncertainty QuantificationPreprint

Ye, F., Yang, M., Pang, J., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.12794 · Cited by 9 · see citing works

Decomposing Uncertainty for Large Language Models through Input Clarification EnsemblingPreprint

Hou, B., Liu, Y., Qian, K., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.08718 · Cited by 4 · see citing works

Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation MitigationPreprint

Rivera, M., Godbout, J., Rabbany, R., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.08694 · Cited by 1 · see citing works

Limitations of large language models in clinical problem-solving arising from inflexible reasoning

Kim, J., Podlasek, A., Shidara, K., Liu, F., Alaa, A., & Bernardo, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-22940-0 · Cited by 78 · see citing works

Uncertainty-aware large language models for explainable disease diagnosis

Zhou, S., Wang, J., Xu, Z., Wang, S., Brauer, D., Welton, L., Cogan, J., Chung, Y.-H., Tian, L., Zhan, Z., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02071-6 · Cited by 27 · see citing works

From tool to teammate in a randomized controlled trial of clinician-AI collaborative workflows for diagnosis

Everett, S., Bunning, B., Jain, P., López, I., Agarwal, A., Desai, M., Gallo, R. J., Goh, E., Kadiyala, V. B., Kanjee, Z., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02545-1 · Cited by 56 · see citing works

The perils of politeness: how large language models may amplify medical misinformation

Rosen, K. L., Sui, M., Heydari, K., Enichen, E. J., & Kvedar, J. C. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02135-7 · Cited by 23 · see citing works

Benchmarking proprietary and open-source language and vision-language models for gastroenterology clinical reasoning

Safavi‐Naini, S. A. A., Ali, S., Shahab, O., Shahhoseini, Z., Savage, T., Rafiee, S., Samaan, J. S., Shabeeb, R. Q. A., Ladak, F., Yang, J. O., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02174-0 · Cited by 25 · see citing works

Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study

Socrates, V., Wright, D. S., Huang, T., Fereydooni, S., Dien, C., Chi, L., Albano, J., Patterson, B., Sasidhar Kanaparthy, N., Wright, C. X., et al. (2025) · JMIR Aging · doi:10.2196/69504 · Cited by 17 · see citing works

From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis

Everett, S. S., Bunning, B. J., Jain, P., Lopez, I., Agarwal, A., Desai, M., Gallo, R., Goh, E., Kadiyala, V. B., Kanjee, Z., et al. (2025) · doi:10.1101/2025.06.07.25329176 · Cited by 18 · see citing works

Token Probabilities to Mitigate Large Language Models Overconfidence in Answering Medical Questions: Quantitative Study

Bentegeac, R., Guellec, B. L., Kuchcinski, G., Amouyel, P., & Hamroun, A. (2025) · Journal of Medical Internet Research · doi:10.2196/64348 · Cited by 19 · see citing works

A longitudinal analysis of declining medical safety messaging in generative AI models

Sharma, S., Alaa, A. M., & Daneshjou, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01943-1 · Cited by 21 · see citing works

Establishing best practices in large language model research: an application to repeat prompting

Gallo, R. J., Baiocchi, M., Savage, T., & Chen, J. H. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae294 · Cited by 14 · see citing works

Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability

Gao, Y., Myers, S., Chen, S., Dligach, D., Miller, T. A., Bitterman, D. S., Chen, G., Mayampurath, A. M., Churpek, M. M., & Afshar, M. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae154 · Cited by 18 · see citing works

Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey.

Ghorbian, M., Ghobaei-Arani, M., & Ghorbian, S. (2025) · Methods · doi:10.1016/j.ymeth.2025.04.001 · Cited by 14 · see citing works

Effective prompt design for large language models in clinical practice

Callens, S. F. J. (2026) · Acta Clinica Belgica · doi:10.1080/17843286.2026.2613903 · Cited by 12 · see citing works

Employing large language models safely and effectively as a practicing neurosurgeon

Patil, A., Serrato, P., Cleaver, G., Limbania, D., See, A. P., & Huang, K. T. (2025) · Acta Neurochirurgica · doi:10.1007/s00701-025-06515-6 · Cited by 8 · see citing works

Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review.

Farrokhi, M., Fallahian, A., Rahmani, E., Aghajan, A., Alipour, M., Khouzani, P. J., Hezarani, H. B., Sabzehie, H., Pirouzan, M., Pirouzan, Z., et al. (2025) · Archives of Academic Emergency Medicine · doi:10.22037/aaemj.v13i1.2712 · Cited by 5 · see citing works

Evaluating large language models as clinical laboratory test recommenders in primary and emergency care: a crucial step in clinical decision making

Zayed, A. M., Frans, G., & Delvaux, N. (2025) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2025-0647 · Cited by 7 · see citing works

Five advanced chatbots solving European Diploma in Radiology (EDiR) text-based questions: differences in performance and consistency

Pristoupil, J., Oleaga, L., Junquero, V., Merino, C., Özbek, S. S., Lambert, L., & European Society of Radiology (ESR) (2025) · European Radiology Experimental · doi:10.1186/s41747-025-00591-0 · Cited by 6 · see citing works

Across generations, sizes, and types, large language models poorly report self-confidence in gastroenterology clinical reasoning tasks

Naderi, N., Safavi‐Naini, S. A. A., Savage, T., Khalafi, M. A., Lewis, P. R., Atf, Z., Nadkarni, G. N., & Soroush, A. (2026) · npj gut and liver. · doi:10.1038/s44355-026-00053-3 · Cited by 8 · see citing works

CeRTS: certainty retrieval token search in large language model clinical information extraction

Schimmelpfennig, L. E., Bhattarai, K., Oh, I. Y., Lever, J., Griffith, O. L., Griffith, M., Lai, A. M., & Abrams, Z. B. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104900 · Cited by 4 · see citing works

Learning Disorder Detection Using Eye Tracking: Are Large Language Models Better Than Machine Learning?

Nguyen, Q., Nguyen, H., Tang, Q.-H., Truong, T., Pham, V., Le, L., & Williams-King, D. (2025) · Eye Tracking Research & Application · doi:10.1145/3715669.3726785 · Cited by 3 · see citing works

Will large language models transform clinical prediction?

Yildiz, Y., Nenadić, G., Jani, M., & Jenkins, D. A. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-025-00211-w · Cited by 4 · see citing works

Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview Study

Alazzawi, M., & Lam, K. K.-L. (2026) · JMIR Formative Research · doi:10.2196/82828 · Cited by 7 · see citing works

Performing Best When Needed Least: Reader Experience Shapes Accuracy Gains in Large Language Model–assisted Brain MRI Differential Diagnosis

Schramm, S., Guellec, B. L., Topka, M., Svec, M., Backhaus, P., Eisenkolb, V. M., Riedel, E. O., Beyrle, M., Platzek, P., Ramschütz, C., et al. (2026) · Radiology · doi:10.1148/radiol.253477 · Cited by 4 · see citing works

AI in Dermato-Oncology: Diagnostic Performance and Prompt-Injection Vulnerability of Vision–Language Models in Dermoscopic Skin Cancer Assessment

Güler, I., Kraus, A., Grieb, G., Satır, T., Eberz, P., & Stelling, H. (2026) · Cancers · doi:10.3390/cancers18111750 · Cited by 3 · see citing works

“MELMA” in otolaryngology: Medical evaluation of large language model answers. Clinician-rated scoring (MELMA-Q) and web-based auditing (MELMA-W) novel tools for AI assessment

Ahmadov, N., Muradova, A., & Aliyeva, A. (2026) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-026-10245-5 · Cited by 5 · see citing works

Calibration of Self-Reported Confidence and Accuracy of Large Language Models in Medical Question Answering

Boie, S. D., Reis, F., Frey, N., Grünewald, E., & Balzer, F. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02430-0 · Cited by 4 · see citing works

AI fatigue in human–AI interaction: Conceptual framework, scale development and validation, and associations with AI engagement

Lau, G. R., Kasturiratna, K. T. A. S., Goh, A. Y. H., Tong, E. M. W., & Hartanto, A. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101186 · Cited by 7 · see citing works

Uncertainty calibration in PHM model from classification to time-series prediction

Pan, J., Jiao, X., Jing, B., et al. (2026) · Measurement · doi:10.1016/j.measurement.2026.120433 · Cited by 2 · see citing works

When machines explain medicine: Nursing ethics and clinical communication

Yang, X., Liang, Y., Liu, Y., Liu, D., Yang, L., Zhang, B., Xu, C., Wu, Y., & He, J. (2026) · Nursing Ethics · doi:10.1177/09697330261449460 · Cited by 4 · see citing works

On-premise medical AI agents for reliable clinical decision-making

Zhang, L., Wölflein, G., Ferber, D., Liang, J., Carrero, Z. I., Wu, X., Vibert, J., Clusmann, J., Möhrmann, L., Möhrmann, E. E., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04609-x · Cited by 2 · see citing works

Structured reasoning failures compromise LLM interpretation of clinical oncology notes

Kenaston, M. W., Ayub, U., Parmar, M., Kumar, P., Anjum, M. U., Naqvi, S. A. A., Chaudhuri, A. A., Zakharia, Y., Heath, E. I., Bekaii-Saab, T. S., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02951-5 · Cited by 2 · see citing works

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

Naderi, N., Atf, Z., Lewis, P. R., Mahjoubfar, A., Safavi‐Naini, S. A. A., & Soroush, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01399-6_5 · Cited by 1 · see citing works

Efficacy of large language models in detecting postoperative delirium from unstructured clinical notes: A retrospective cohort study

Eun, D.-I., Lee, H.-C., Heo, G., Jeong, J. H., Lee, J., Lee, H., Park, S., Nam, K., Lee, H.-J., & Ju, J. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02231-8 · Cited by 1 · see citing works

Comparative Performance of ChatGPT-4o, Grok-3, and Claude 3.7 Sonnet in Differentiating Cerebellopontine Angle Schwannomas and Meningiomas: A Pilot Study of Visual Versus Text-Based Diagnostic Prompting

Karaman, A. K., Yılmaz, B., Acar, K. H., Alizada, S., Korkmazer, B., Kartum, T. A., Samancı, C., Dağdelen, M., Çomunoğlu, N., Arslan, S. A., et al. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01793-z · Cited by 1 · see citing works

CALM-RAD: Calibrated GPT-4o Confidence-Based Triage Enables 96–100% Accuracy in Automated TNM Staging of Head and Neck Cancer Reports

Gupta, A. K., Adams, L. C., & Rangarajan, K. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01904-4 · Cited by 1 · see citing works

Engineering framework for curiosity-driven and humble AI in clinical decision support

Arslan, J., Benke, K. K., Cajas, S. A., Castro, R., Celi, L. A., Cruz-Suarez, G. A., Reyes, R. D., Engelmann, J., Ercole, A., Hilel, A., et al. (2026) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2025-101877 · Cited by 1 · see citing works

Assessing the clinical reasoning of large language models on complex rheumatology cases: A multidimensional evaluation of four artificial intelligence

Bayala, Y. L. T., Kaboré, F., Sougué, C., Ouédraogo, A., Zongo, E., Tiendrébeogo, W. J. S. Z., & Ouedraogo, D. (2026) · Health Informatics Journal · doi:10.1177/14604582261448687 · 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

A roadmap for medical large language models: a review of foundations, applications, and challenges

Sha, Y.-Y., Yu, L., Lin, Z.-H., Kaur, A., Lou, Y., Gornale, S. S., Zhang, T., Wong, L. S., Wang, Z., Yan, Y., et al. (2026) · Military Medical Research · doi:10.1016/j.mmr.2026.100050 · Cited by 2 · see citing works

Hallucinations in generative artificial intelligence and large language models: tests, datasets, detection and correction methods

Naser, M. Z. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09938-4 · Cited by 2 · see citing works

Neuro-symbolic information fusion for explainable retrieval-augmented generation in mental health applications: A survey and challenges

Kang, X., Ding, W., Matsumoto, K., Wang, L., Yu, H.-T., & Shi, X. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104702 · Cited by 1 · see citing works

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation

Atf, Z., & Lewis, P. R. (2025) · IEEE International Conference on Collaborative Advances in Software and Computing (CASCON) · doi:10.1109/cascon66301.2025.00018 · Cited by 1 · see citing works

The economics of accuracy for medical reasoning with large language models

Bhattacharyya, K., & Kamabattula, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001182

An Information-Theoretic Perspective on Multi-LLM Uncertainty EstimationPreprint

Kruse, M., Afshar, M., Khatwani, S., Mayampurath, A. M., Chen, G., & Gao, Y. (2025) · medRxiv · doi:10.1101/2025.07.09.25331207

Exploring the prognostic utility of large language models versus traditional clinical models in heart failure: a pilot study

Shang, L., Chen, Y., Li, R., Zhang, X., Gao, M., Hou, Y., & Zhang, G. K. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000004062

Self-Aware Language Models: A Taxonomy and Evaluation of Epistemic Uncertainty and Hallucination MitigationPreprint

Tiwari, A., & Gupta, V. (2026) · Research Square · doi:10.21203/rs.3.rs-8589677/v1

Artificial intelligence-facilitated evidence-based medicine research and practice: A position statement

AI4Evidence Working Group of the Chinese Medical Doctor Association (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003923

Developing and Testing an Engineering Framework for Curiosity-Driven and Humble AI in Clinical Decision SupportPreprint

Arslan, J., Benke, K. K., Cajas, S. A., Castro, R., Celi, L. A., Cruz-Suarez, G. A., Reyes, R. D., Engelmann, J., Ercole, A., Hilel, A., et al. (2026) · medRxiv · doi:10.64898/2026.02.06.26345664

Development and retrospective validation of SCOUT: scalable clinical oversight of large language models via uncertainty triangulationPreprint

Ba, Z., He, M., He, H., Fu, Q., Lai, J. Z., Zhang, R., Diao, X., Liu, M., wang, Z., Wang, X., et al. (2026) · medRxiv · doi:10.64898/2026.02.08.26345860

Preconditions for responsible artificial intelligence adoption in psychiatry: commentary, Armitage

Armitage, R. C. (2026) · The British Journal of Psychiatry · doi:10.1192/bjp.2026.10560

Exploring probability-aware parametric softmax for model calibration in image classification

Zhang, S., Xie, L., & Chen, Y. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113488

Evaluating the Performance of ChatGPT and DeepSeek in Bilingual Responses to Questions Regarding Craniofacial Microsomia

Li, L., Zhao, S., Feng, S., Liu, W., & Zhang, Z. (2026) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000012589

From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs

Markatou, M., Mukhopadhyay, R., Good, J., Tan, Y., Dharia, A., Kennedy, O., Brown, L. S., & Talal, A. H. (2026) · Journal of Medical Internet Research · doi:10.2196/97131

Reply to Letter to the Editor

Makrygiannakis, M. A., Giannakopoulos, K., & Kaklamanos, E. G. (2026) · European Journal of Orthodontics · doi:10.1093/ejo/cjag010

Letter to the Editor

Dashti, M., Khosraviani, F., & Golrang, E. (2026) · European Journal of Orthodontics · doi:10.1093/ejo/cjag009

Prompt-Induced Output Variability and Structured-Output Integrity in Local Open Large Language Models: A Multi-model In Silico Benchmark Using Synthetic Acute-Care Scenarios

Harada, Y. (2026) · Cureus · doi:10.7759/cureus.108768 · Cited by 2 · see citing works

Case Studies of Explainable LLMs in Diagnosis, Treatment Planning, and Patient Interaction

Tabatabaei, A., Gorji, M., & Rahimi, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_8

Development and External Validation of a Large Language Model–Based Clinical Decision-Support System for Colonoscopy Surveillance Intervals

Rao, A., Bali, A., Tran, V. V., Vasavada, S., Chu, J., Kanwal, F., Rusin, C., & El‐Serag, H. B. (2026) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2026.05.017

Benchmarking reliability and calibration of LLMs for multi-cancer early detection test communication

Takabatake, K., Rosito, M. S., Braun, D., Marinac, C. R., O’Donnell, E., & Parmigiani, G. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag093

Calibrated Abstention and Clinical Deferral in Small Language Models via RLVR and GRPOPreprint

Wibisono, N. B. (2026) · Research Square · doi:10.21203/rs.3.rs-10198691/v1

Benchmarking Multimodal Large Language Models for Cardiopulmonary Findings on Chest Radiographs: Sex-Stratified Discrimination and Operating Characteristics

Haupt, M., Bischoff, A., Atoubi, M., Thomas, R. P., & Maurer, M. H. (2026) · Diagnostics · doi:10.3390/diagnostics16132131

Diagnostic performance of artificial intelligence in the syncope unit

van Zanten, S., Boel, T. T., de Jong, J. S. Y., Koomen, E. M., Bais, B., Dara, A., Giele, F., Geertsma, C., Sutton, R., Scheffer, M. G., et al. (2026) · Exploration of Cardiology · doi:10.37349/ec.2026.1012114

A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health recordsPreprint

Yang, Z., Zhang, Y., Love, Z., et al. (2026) · medRxiv · doi:10.64898/2026.07.16.26358271

Designing for Complementary Vulnerabilities: A Governance Framework for Deploying Large Language Models in Clinical Decision-Making (Preprint)Preprint

Amoei, M., Fraser, K., & Poenaru, D. (2026) · doi:10.2196/preprints.107982

Towards Robust Clinical Reasoning in LLM-aided Clinical Decision Support

Vuruma, S. K. R., Tang, S., & Wu, D. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00148

Evidence-Bound Generation for Medicines Information with Verifiable Claims and Visible Uncertainty

Ferraro, L., Ricci, M., (1560388), G. M., & Greco, P. (2026) · Archives of Pharmacy Practice · doi:10.51847/tzcg1ksqsz

S-BEED: Sparse Bayesian Ensemble with Entropy-Calibrated Debate for Medical Multiple-Choice Question AnsweringPreprint

Dan, W., Xi, S., Guo, C., Ran, X., & Qin, Z. S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7305416

Inference-Time Diagnostic Instructions and Diagnostic Reliability in Large Language Models: Cross-Model and Cross-Dataset Evaluation (Preprint)Preprint

Shao, M., & Zhang, H. (2026) · doi:10.2196/preprints.109533

Applying Artificial Intelligence to a Complex Diagnostic Challenge: A CNS Germ Cell Tumor Presenting as Insidious Memory Loss—A Systematic Evaluation of Large Language Models’ Reasoning Abilities across Multi-Stage Complex Clinical Workups

高, 同. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1682938

Conditional Perplexity Scoring for Large Language Model−Generated Differential Diagnoses in Case Reports: Preliminary Computational Evaluation

Hirosawa, T., Harada, Y., Kawamura, R., & Shimizu, T. (2026) · JMIR Formative Research · doi:10.2196/98819

Comparative performance of contemporary multimodal large language models in retinal imaging question answering

Gu, X., Yang, Y., Lin, X., & Xu, C. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1934968

Language Models for Value-Informed Proxy Decision Support

Nolan, V. J., Brown, M. M., Balch, J. A., Katukota, A. V., Abbas, S., Devaguptapu, P., Baskaran, N., Abbott, K. L., Hong, P. K. W., Shickel, B., et al. (2026) · NEJM AI · doi:10.1056/aics2500562

Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification

Tilley, A., Murray, B., Henry, K., et al. (2026) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2026.32.9.1076

Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions

Lei, Q.-K., Wu, T.-X., Zhang, C.-L., & Zheng, Y. (2026) · Liver Research · doi:10.1016/j.livres.2026.08.001

JudgEHR: LLM-Guided Cohort Inference for Clinical Decision Confidence Estimation

Xin, K., Pelat, G., Vitale, J., Takeuchi, S., Berkovsky, S., & Beheshti, A. (2026) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh72779.2026.00046

Transforming large language models into medical specialists via knowledge injection

Kim, K., Song, J., Kim, D. Y., Nam, Y., Park, S., Shin, K., Jo, W., & Kim, N. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.103020

Comparative evaluation of large language models for text-based diagnostic reasoning in fetal central nervous system MRI

Yu, R., Peng, M., Li, H., Liu, S., Su, T., Guan, H., Shen, Y., Deng, Y., & Zhao, D. (2026) · Medicine · doi:10.1097/md.0000000000050485

From scoring to stress testing: strengthening safety validation of large language model answers in otolaryngology

Yin, L., Wang, Y., Dai, Z., et al. (2026) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-026-10589-y

Revision Behavior and Explainability in Adaptive LLM Swarms for ICU Mortality Risk Prediction: A Two-Dataset EvaluationPreprint

Anderson, C. (2026) · medRxiv · doi:10.64898/2026.09.01.26361960

Potenzial und Herausforderungen von Large Language Models in der klinischen Entscheidungsfindung

Jungmann, F., & Hager, P. (2026) · Journal für Gastroenterologische und Hepatologische Erkrankungen · doi:10.1007/s41971-026-00265-7

Large language models for ophthalmic examination understanding: from information extraction to clinical decision support

Wang, G., Lin, X., & Yang, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1937970

What Must Physicians Still Know? Cognitive Externalizability as a Framework for Medical Education in the Age of AI (Preprint)Preprint

Crespo, J., Carballo, F., & Turnés, J. (2026) · doi:10.2196/preprints.112174

Generative AI in Pharmacotherapy: Why Evidence Traceability, Verification Burden, Uncertainty, and Human Override Define the Boundary Between Clinical Assistance and Medication-Safety Risk

Wilson, E., Jong, F. D., & Peters, L. (2026) · Annals of Pharmacy Practice and Pharmacotherapy · doi:10.51847/xt3dqdt8kp

Avoiding common failures in AI for health and medicine

Salaudeen, O., Zhang, H., Kim, E., Singh, K., & Ghassemi, M. (2026) · Cell · doi:10.1016/j.cell.2026.07.025

Performance of large language models on narrow therapeutic index drug monitoring: Implications for clinical pharmacy practice

Azmakan, H., & Azamakan, T. (2026) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2026.103524

The missing links in agentic AI autonomy

Omar, M., Abdulnour, R.-E. E., & Nadkarni, G. N. (2026) · Nature Medicine · doi:10.1038/s41591-026-04658-2

Toward an epistemic grounding of clinical reasoning: artificial intelligence and psychological diagnosis

Pierotti, A. P., & Cremaschi, M. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1952196

Calibrated Reliance on Large Language Models for Personalized Surgical Care: A Critical Narrative Review of Confidence, Clinical Reasoning and Decision Support

Barone, M., Selvaggi, F., Pasquale, V., Nusrath, S., Vashist, Y. K., & Marano, L. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15207802

Sycophancy in Large Language Models in Oral and Maxillofacial Radiology: The Impact of Misleading Context on Image- and Text-Based Diagnostic Scenarios

Geduk, G., Çapar, İ., & Hasırcı, U. C. (2026) · Diagnostics · doi:10.3390/diagnostics16203296

Medical Fine-Tuning Reshapes, Not Reduces, Patient-Facing AI Risk: A Rubric-Based, LLM-Judge Evaluation Framework

Doshi, S. (2026) · arXiv · doi:10.48550/arxiv.2610.24807

Accurate And Fast Fine-Grained Image Classification via Discriminative Learning

Wang, Z., Wang, S., Zhang, P., Li, H., & Liu, B. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00115 · Cited by 3 · see citing works

Stable Discriminative Dictionary Learning Via Discriminative Deviation

Khan, N., & Tappen, M. F. (2012) · Journal of International Crisis and Risk Communication Research · Cited by 6 · see citing works

Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences

Moghimi, M. K., Kerr, J., Johnson, E., Godbole, S. V., & Belongie, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14442-9_42 · Cited by 2 · see citing works

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Kangbo, S. U. N., & Zhu, J. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7094 · Cited by 6 · see citing works

Fast Fine-Grained Image Classification via Weakly Supervised Discriminative Localization

He, X., Peng, Y., & Zhao, J. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2834480 · Cited by 88 · see citing works