Large language model AI chatbots require approval as medical devices

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

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

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

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

What ChatGPT and generative AI mean for science

Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828

Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery

Korngiebel, D. M., & Mooney, S. D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00464-x · Cited by 259

The effect of Dr Google on doctor–patient encounters in primary care: A quantitative, observational, cross-sectional study

Van Riel, N., Auwerx, K., Debbaut, P., Van Hees, S., & Schoenmakers, B. (2017) · BJGP Open · doi:10.3399/bjgpopen17x100833 · Cited by 159

AI chatbots are coming to search engines — can you trust the results?

Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00423-4 · Cited by 23

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review

van der Meijden, S. L., van Boekel, A. M., van Goor, H., Nelissen, R. G. H. H., Schoones, J. W., Steyerberg, E. W., Geerts, B. F., de Boer, M. G. J., & Arbous, M. S. (2024) · JMIR Medical Informatics · doi:10.2196/57195 · Cited by 12

Performance Assessment of Large Language Models in Medical Consultation: Comparative Study

Seo, S., Kim, K., & Yang, H. (2025) · JMIR Medical Informatics · doi:10.2196/64318 · Cited by 12

If a therapy bot walks like a duck and talks like a duck then it is a medically regulated duck

Ostermann, M., Freyer, O., Verhees, F., Kather, J., & Gilbert, S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02175-z · Cited by 9

Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology

Thieme, A., Rajamohan, A. M., Cooper, B., Groombridge, H., Simister, R. J., Wong, B., Woznitza, N., Pinnock, M. A., Wetscherek, M. T., Morrison, C. P., et al. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3716500 · Cited by 12

Evaluating a Chatbot as a Companion for Patients With Breast Cancer: Collaborative Pilot Study

Boie, S. D., Glastetter, E., Lux, M. P., Balzer, F., Kalle, C. V., Lenz, C., & Müller, U. (2025) · JMIR Cancer · doi:10.2196/68426 · Cited by 13

Enhancing Human-Computer Interaction in Socially Inclusive Contexts: Flow Heuristics and AI Systems in Compliance with DIN EN ISO 9241 Standards

Vindigni, G. (2024) · European Journal of Contemporary Education and E-Learning · doi:10.59324/ejceel.2024.2(4).10 · Cited by 19

Enhancing risk and crisis communication with computational methods: A systematic literature review

Munro, M. H., Gore, R. J., Lynch, C. J., Hastings, Y. D., & Reinhold, A. M. (2024) · Risk Analysis · doi:10.1111/risa.17690 · Cited by 9

Consternation as Congress proposal for autonomous prescribing AI coincides with the haphazard cuts at the FDA

Gilbert, S., Dai, T., & Mathias, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01540-2 · Cited by 9

Chat, Summary and Diagnosis: A LLM - Enhanced Conversational Agent for Interactive Depression Detection

Zhang, X., Cui, W., Wang, J., & Li, Y. (2024) · International Conference on Industrial Automation, Robotics and Control Engineering (IARCE) · doi:10.1109/iarce64300.2024.00070 · Cited by 10

Technosolutionism and the empathetic medical chatbot

Sharon, T. (2025) · AI & Society · doi:10.1007/s00146-025-02441-4 · Cited by 12

Evaluating Web Retrieval–Assisted Large Language Models With and Without Whitelisting for Evidence-Based Neurology: Comparative Study

Masanneck, L., Epping, P. Z., Meuth, S. G., & Pawlitzki, M. (2025) · Journal of Medical Internet Research · doi:10.2196/79379 · Cited by 9

PHM–GPT: A Large Language Model for Prognostics and Health Management

Ren, J., Liu, X., Wang, T., Zhao, Z., Chen, X., Li, W., & Yan, R. (2025) · Engineering · doi:10.1016/j.eng.2025.11.001 · Cited by 9

Artificial intelligence characters are dangerous without legal guardrails

Duffourc, M. N., Verhees, F. G., & Gilbert, S. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02375-3 · Cited by 8

A Study of Generative Large Language Model for Healthcare

Park, S.-G., Kim, A., Yoon, T., Kamyod, C., & Kim, C. G. (2023) · doi:10.1109/incit60207.2023.10412989 · Cited by 9

Overview of South Korean Guidelines for Approval of Large Language or Multimodal Models as Medical Devices: Key Features and Areas for Improvement

Park, S. H., Dean, G., Ortiz, E. M., & Choi, J. (2025) · Korean Journal of Radiology · doi:10.3348/kjr.2025.0257 · Cited by 8

Comparing physician and large language model responses to influenza patient questions in the online health community

Wu, H. M., Li, M., & Zhang, L. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105836 · Cited by 9

Rapid deployment of large language model DeepSeek in Chinese hospitals demands a regulatory response

Shen, T., Li, Y., Cao, Y., Du, X., Wang, X., Zhang, Y., & Zhang, Y. J. (2025) · Nature Medicine · doi:10.1038/s41591-025-03836-y · Cited by 7

Digital medicine’s international race for regulatory sandboxes and voluntary alternative pathways picks up tempo

Gilbert, S. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02414-x · Cited by 7

MS360°: a conceptual digital-first, data-driven hybrid care framework for personalised multiple sclerosis management

Voigt, I., Masanneck, L., Pawlitzki, M., Inojosa, H., Meuth, S. G., & Ziemssen, T. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02461-4 · Cited by 8

Challenges and Proposed Additional Considerations for Medical Device Approval of Large Language Models Beyond Conventional AI

Park, S. H., & Kim, N. (2024) · Radiology · doi:10.1148/radiol.241703 · Cited by 7

Real-World Performance of Large Language Models in Emergency Department Chest Pain TriagePreprint

Meng, X., Ji, J., Yan, X., Xu, H., Gao, J., Wang, J., Wang, J., Wang, X., Wang, Y.-G.-S., Wang, W., et al. (2024) · medRxiv · doi:10.1101/2024.04.24.24306264 · Cited by 7

Generative AI-Enabled Therapy Support Tool Improves Clinical Outcomes and Patient Engagement in NHS Talking Therapies

Habicht, J., Dina, L. M., Stylianou, M., Harper, R., Hauser, T. U., & Rollwage, M. (2024) · doi:10.31234/osf.io/mj46k · Cited by 7

Evaluating the quality and empathy of responses to patient questions on the Korean Academy of Periodontology’s online question and answer section: a cross-sectional study comparing periodontists and an AI-powered chatbot

Lee, J., Oh, S. E., Schwendicke, F., Chaurasia, A., & Kim, Y. (2025) · Journal of Periodontal & Implant Science · doi:10.5051/jpis.2402220111 · Cited by 6

Using conversant artificial intelligence to improve diagnostic reasoning: ready for prime time?

Scott, I. A., Miller, T., & Crock, C. (2024) · The Medical Journal of Australia · doi:10.5694/mja2.52401 · Cited by 6

Improved precision oncology question-answering using agentic LLMPreprint

Das, R., Maheswari, K., Siddiqui, B. S., Arora, N., Paul, A., Nanshi, J., Udbalkar, V., Sarvade, A., Chaturvedi, H., Shvartsman, T., et al. (2024) · medRxiv · doi:10.1101/2024.09.20.24314076 · Cited by 6

Derivation and external validation of a portable method to identify patients with pulmonary embolism from radiology reports: The READ-PE algorithm

Christensen, M. A., Stubblefield, W., Wang, G., Altheimer, A., Ouadah, S. J., Birrenkott, D., Peters, G. A., Prucnal, C., Harshbarger, S., Chang, K. E., et al. (2024) · Thrombosis Research · doi:10.1016/j.thromres.2024.109105 · Cited by 6

AI for evidence-based treatment recommendation in oncology: a blinded evaluation of large language models and agentic workflows

Zhai, G., Bar, M., Cowan, A. J., Rubinstein, S. M., Shi, Q., Zhang, N., Xie, E., & Ma, W. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1683322 · Cited by 9

Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study

Reis, F., Agha-Mir-Salim, L., Hickstein, R., Reis, M., Piper, S. K., Balzer, F., & Boie, S. D. (2026) · Journal of Medical Internet Research · doi:10.2196/84668 · Cited by 8

GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts.Preprint

Wu, D., Yang, J., Liu, C., Hsieh, T., Marchi, E. J., Blair, J., Krawitz, P. M., Weng, C., Chung, W. K., Lyon, G. J., et al. (2024) · PubMed · Cited by 5

Rx-LLM: a benchmarking suite to evaluate safe large language model performance for medication-related tasksPreprint

Zhao, X., Blotske, K., Cargile, M., Tilley, A., Murray, B., Gao, Y., Henry, K., Smith, S. E., Barreto, E. F., Bauer, S. R., et al. (2025) · medRxiv · doi:10.64898/2025.12.01.25341004 · Cited by 9

Benchmarking GPT-5 in radiation oncology: measurable gains, but persistent need for expert oversight

Dinç, U., Sarkar, J., Schubert, P., Semrau, S., Weißmann, T., Karius, A., Brand, J., Axer, B., Gomaa, A., Stephan, P., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1695468 · Cited by 5

Large language models in neuropsychology: Emerging applications and ethical considerations

Kronenberger, O. R., Gottlieb, M. C., & Cullum, C. M. (2025) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2604094 · Cited by 4

The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems

Freyer, O., Mathias, R., Muti, H. S., Orlovsky, H., Buch, S., Ostermann, M., Schönfelder, A., Poncette, A.-S., Bassily‐Marcus, A., & Gilbert, S. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02535-3 · Cited by 4

The current status and prospects of large language models in medical application and research

Deng, H., Zhou, Q., Zhang, Z., Zhou, T., Lin, X., Xia, Y., Fan, L., & Liu, S. (2024) · Chinese Journal of Academic Radiology · doi:10.1007/s42058-024-00164-x · Cited by 6

European regulation of digital respiratory healthcare

Gilbert, S. (2023) · European Respiratory Society eBooks · doi:10.1183/2312508x.10000923 · Cited by 5

„Creating God“

Rothenhäusler, A. (2024) · De Gruyter eBooks · doi:10.1515/9783111351490-013 · Cited by 4

Adaptive Human-LLMs Interaction Collaboration: Reinforcement Learning driven Vision-Language Models for Medical Report Generation

Cao, Y., Li, Z., Cui, L., & Miao, C. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719852 · Cited by 3

Walking Backward to Ensure Risk Management of Large Language Models in Medicine

Onitiu, D., Wachter, S., & Mittelstadt, B. (2025) · The Journal of Law Medicine & Ethics · doi:10.1017/jme.2025.10132 · Cited by 3

Adoption rates and knowledge of generative artificial intelligence in pharmacy practice: A comparative study in an internet-restricted country

Kahwaji, A., Ismael, R., Arnouk, T., Alhomsy, A., & Alsuliman, T. (2026) · Digital Health · doi:10.1177/20552076261432730 · Cited by 3

Vision–language foundation model driven agentic AI systems for healthcare

Chen, L., Xu, X., Qin, Y., Li, H., & Jiang, N. (2026) · The Visual Computer · doi:10.1007/s00371-026-04437-7 · Cited by 3

The Impact of AI Trust Violation on Trustworthiness: An Empirical Study Based on AI Chatbots

Wu, L., Xiang, P., Chen, Y., Li, J., Cao, Z., & Qi, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-5803-6_27 · Cited by 3

Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices

Watt, T., Chrysoulas, C., Barclay, P. J., Boudani, B. E., & Kalliatakis, G. (2024) · Applied Sciences · doi:10.3390/app142411474 · Cited by 3

The regulatory status of health apps that employ serious games and gamificationPreprint

Gilbert, S., Freyer, O., Wrona, K. J., de Snoeck, Q., Hoffmann, M., Melvin, T., Stratton‐Powell, A. A., Wicks, P. J., & Parks, A. C. (2024) · Research Square · doi:10.21203/rs.3.rs-3731773/v1 · Cited by 3

The Emerging Role of ISO 42001 Certification in Fostering the Deployment of Responsible Generative AI Healthcare SolutionsPreprint

Thiers, F. A., & Harned, Z. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/5ks37 · Cited by 6

Not All Tokens Are Equal: Membership Inference Attacks Against Fine-tuned Language Models

Song, C., Zhao, D., & Xiang, J. (2024) · Annual Computer Security Applications Conference · doi:10.1109/acsac63791.2024.00020 · Cited by 3

Development and Reliability Assessment of an Artificial Intelligence-Driven Urticaria Support (AIDUS) Chatbot

Aulenbacher, F., Gutsche, A., Bihlmaier, B., Bonnekoh, H., Chérrez-Ojeda, I., Fluhr, J. W., Kolkhir, P., Magerl, M. A. K., Metz, M., Pyatilova, P., et al. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.07.047 · Cited by 2

How to bring generative AI to oncology practice

Truhn, D., & Kather, J. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2025.100679 · Cited by 2

Generative Artificial Intelligence and Large Language Models in Clinical Oncology

Yu, Y., Zhao, Z., Wang, Z., Lin, R., Tan, Y., Chen, Y., Li, T., Baptista‐Hon, D. T., Zhang, X., Wu, C., et al. (2026) · MedComm · doi:10.1002/mco2.70833 · Cited by 2

Die kommende Entwicklung großer Sprachmodelle in der Medizin

Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J., Laleh, N. G., Loeffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., et al. (2024) · Kompass Onkologie · doi:10.1159/000536600 · Cited by 2

An empirical study on prediction of seismic activity using stochastic configuration networks

Qiu, Y., & Wang, D. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10244-9 · Cited by 2

Consideration of Cybersecurity in the Benefit-Risk Analysis of Medical Devices: A Scoping Review and RecommendationsPreprint

Freyer, O., Jahed, F., Ostermann, M., Rosenzweig, C., Werner, P., & Gilbert, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4816554/v1 · Cited by 2

A Distinct Approach to Clinical GenAI OversightPreprint

Thiers, F. A., & Lucy, K. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/vm6zy · Cited by 2

Zur Entwicklung des Medizinhaftungsrechts

Hart, D. (2024) · Medizinrecht · doi:10.1007/s00350-024-6735-6 · Cited by 1

PRISM-Med: Parameter-Efficient Robust Interdomain Specialty Model for Medical Language Tasks

Kang, J., Ryu, H., & Sim, J. (2025) · IEEE Access · doi:10.1109/access.2024.3525041 · Cited by 1

Artificial Intelligence in Orthodontics Diagnosis and Treatment

Majstorović, N. V., & Dimitrijević, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95197-8_7 · Cited by 1

Comparative analysis of open-source locally deployed LLMs for extraction of diagnostic information from clinical CMR imaging reports

Amirrajab, S., Vehof, V., Bietenbeck, M., & Yılmaz, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-75150-7 · Cited by 2

Evaluating Large‐Scale and Lightweight Large Language Models for Traditional Chinese Medicine Exam Questions: A Comparative Study

Li, Y., Huang, S., Qi, J., Lü, Y., Quan, L., Han, D., Li, B., Liu, X., & Luan, Z. (2026) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70118 · Cited by 3

From policy to practice: Evaluating the global implications of the FDA’s PDURS framework

Zhang, Y., Shen, L., He, S., Wu, R., Wang, J., Zong, H., Ullah, A., Liu, X., Chen, A., Fox, D., et al. (2026) · Digital Health · doi:10.1177/20552076261438570 · Cited by 1

A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives

Tang, W., Zhou, J., Chen, W., Xie, Y., Zhang, L., Chen, S., Hou, X., Wang, S., & Shi, H. Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-50745-2 · Cited by 1

Large language models and large multimodal models in radiology: opportunities, challenges, and the path toward sustainable long-term clinical integration

Chow, J., & Patlas, M. N. (2026) · Japanese Journal of Radiology · doi:10.1007/s11604-026-02009-2 · Cited by 1

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

AI Mental Health Support and the Case for Distributed Networked Regulation

Paterson, J. (2026) · Cambridge University Press eBooks · doi:10.1017/9781009860796 · Cited by 1

Is word order considered by foundation models? A comparative task-oriented analysis

Zhao, Q., Li, J., Liu, J., Kang, Z., & Zhou, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122700 · Cited by 3

What Comes Next for Large Language Models in Medicine?

Laynor, G. (2024) · Journal of Electronic Resources in Medical Libraries · doi:10.1080/15424065.2024.2320227 · Cited by 1

Regulação da inteligência artificial na saúde

Dourado, D. D. A. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2024.tde-23042024-111255 · Cited by 2

A pen mark is all you need - Incidental prompt injection attacks on Vision Language Models in real-life histopathologyPreprint

Clusmann, J., Schulz, S., Ferber, D., Wiest, I. C., Fernandez, A., Eckstein, M., Lange, F., Reitsam, N. G., Kellers, F., Schmitt, M. P., et al. (2024) · medRxiv · doi:10.1101/2024.12.11.24318840 · Cited by 1

MedMatch: a standardized medication order format for data science and natural language processing applicationsPreprint

Blotske, K., Keats, K. R., Zhao, X., Li, T., Tilley, A. R., Cargile, M., Murray, B., Gao, Y., Smith, S. E., Barreto, E. F., et al. (2026) · medRxiv · doi:10.64898/2026.01.13.26343949 · Cited by 1

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

Impact of digital assistive technologies on the quality of life for people with dementia: A scoping reviewPreprint

Schneider, C., Nißen, M., Kowatsch, T., & Vinay, R. (2023) · medRxiv · doi:10.1101/2023.10.02.23296434

Human-Centered AI: Large Language Models and the Need for Ethical Medical Chatbots (Preprint)Preprint

Chow, J. C. L., & Li, K. (2024) · doi:10.2196/preprints.56404

Einsatzmöglichkeiten von „large language models“ in der Onkologie

Loeffler, C. M. L., Bressem, K. K., & Truhn, D. (2024) · Die Onkologie · doi:10.1007/s00761-024-01481-7

Real-World Performance of Large Language Models in Emergency Department Chest Pain Triage and the development of MediGuide-14BPreprint

Tang, Y. (2024) · Research Square · doi:10.21203/rs.3.rs-4785726/v1

Machine Learning and Neural NLG

Reiter, E. (2024) · Natural Language Generation · doi:10.1007/978-3-031-68582-8_3

Interpretable AI for medical image analysis: methods, evaluation, and clinical considerations

Gonçalves, T., Hedström, A., de Mortanges, A. P., Li, X., Müller, H., Cardoso, J. S., & Reyes, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00027-4

Centering Equity in Health AI: LLM Design Strategies for Impactful Solutions (Preprint)Preprint

Dewitt, A., Parker, A. G., Harrington, C., & Cole-Lewis, H. J. (2025) · doi:10.2196/preprints.71727

Patient-centered Evaluation of AI Answers to Genetic Counseling QuestionsPreprint

Huang, Y., Kambhamettu, H., Wood, E. M., Cacioppo, C. N., Ofidis, D., Mim, R., Bradbury, A. R., & Johnson, K. B. (2025) · medRxiv · doi:10.1101/2025.03.27.25323779

Is My LLM Application Considered a Medical Device under the MDR?Preprint

Workum, J. D., Krijgsman, S., Vajda, I., van Stokkum, R., van den Brand, J. A. J. G., Gommers, D. A. M. P. J., & van Genderen, M. E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5202088

Future utility of generative artificial intelligence in colorectal surgery

Yiu, A. J., & Lam, K. (2025) · Frontline Gastroenterology · doi:10.1136/flgastro-2024-103023

Wie bringen wir die KI auf die Straße?

Kather, J. N. (2025) · Springer eBooks · doi:10.1007/978-3-662-71042-5_8

Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models

Khanmohammadi, R., Ghassemi, M. M., Verdecchia, K., Ghanem, A. I., Luo, B., Chetty, I. J., Bagher‐Ebadian, H., Siddiqui, F., Elshaikh, M. A., Movsas, B., et al. (2025) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubaf010 · Cited by 7

From General Intelligence to Specialized Support: AIDUS as a Blueprint for Artificial Intelligence in Chronic Urticaria

Kakumanu, S., & Portnoy, J. M. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.08.018

Künstliche Intelligenz in der Diagnostik und Behandlung des kolorektalen Karzinoms

Kather, J. N. (2025) · Die Onkologie · doi:10.1007/s00761-025-01832-y

Digitale Patientenkommunikation und LLM-basierte Gesprächsagenten

Gilbert, S., & Freyer, O. (2025) · Springer eBooks · doi:10.1007/978-3-662-71034-0_10

Structured panendoscopy reports improve report completeness and documentation time

Bärhold, F., Wolpert, S., Ernst, B. P., Potthast, G., Breuer, T., Becker, S., Pummerer, L., & Holderried, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-27738-8

A chatbot-delivered stress management coaching for students (MISHA app) : pilot randomized controlled trial

Ulrich, S., Lienhard, N., Künzli, H., & Kowatsch, T. (2024) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-30977

Additional file 1 of Digital clinical empathy in a live chat: multiple findings from a formative qualitative study and usability tests

Luetke Lanfer, H., Reifegerste, D., Weber, W., Memenga, P., Baumann, E., Geulen, J., Klein, S., Müller, A., Hahne, A., & Weg‐Remers, S. (2024) · Figshare · doi:10.6084/m9.figshare.26690860

NoRMA: A Multi-agent Communication-Centric Dataset for Enhanced Customs Nomenclature Classification

Bouchtib, H., Karboub, K., Tabaa, M., & Hamlich, M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15632-7_18

Artificial intelligence-enabled personalized medicine: strategies and challenges

Chavda, V. P., Vyas, A., Rathore, N., Sheth, T. J., Shah, N., & Shah, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00017-8

Large language models in healthcare

Truhn, D., & Kather, J. N. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01071-3

RECALL-LO: Enhancing Label-Only Membership Inference Against Large Language Models

Liu, Y., Song, S., Zhou, C., & Zhou, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461598

Introducing Adjunct Medical Products and Artificial Intelligence Governance Gaps

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Beyond the Response: Examining Reasoning and Execution Fidelity in Large Language Models for Mental Health

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Digital Health in Reproductive Rheumatology

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Performance of Large Language Model-Based Chatbots in Primary Health Care Teleconsultations: A Comparison Between Human and Artificial Intelligence-Generated Responses

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Theoretical Exploration of Error Thresholds for Clinical AI Decision Support in Nursing: Exploratory Simulation Study Grounded in Human-AI Reliance Data

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Chatbots in medicine: certification process and applied use case

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Pant, N., & Mehrotra, A. (2026) · doi:10.2196/preprints.114925

Unregulated large language models produce medical device-like output

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