AI chatbots not yet ready for clinical use.
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Abstract
As large language models (LLMs) expand and become more advanced, so do the natural language processing capabilities of conversational AI, or "chatbots". OpenAI's recent release, ChatGPT, uses a transformer-based model to enable human-like text generation and question-answering on general domain knowledge, while a healthcare-specific Large Language Model (LLM) such as GatorTron has focused on the real-world healthcare domain knowledge. As LLMs advance to achieve near human-level performances on medical question and answering benchmarks, it is probable that Conversational AI will soon be developed for use in healthcare. In this article we discuss the potential and compare the performance of two different approaches to generative pretrained transformers-ChatGPT, the most widely used general conversational LLM, and Foresight, a GPT (generative pretrained transformer) based model focused on modelling patients and disorders. The comparison is conducted on the task of forecasting relevant diagnoses based on clinical vignettes. We also discuss important considerations and limitations of transformer-based chatbots for clinical use.
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Hallucinations are well described in transformer-based generative models, including ChatGPT.
“hallucinations are also well described in transformerbased generative models, 24,25 including the recent ChatGPT”
From Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study (2024) · cites this paper as [25]
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Mapping the individual, social and biospheric impacts of Foundation Models
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Large language models and rheumatology: are we there yet?
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Guidelines For Rigorous Evaluation of Clinical LLMs For Conversational ReasoningPreprint
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Healthcare: A Growing Role for Large Language Models and Generative AI
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Empowering Preventive Care with GECA Chatbot
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Park, K. U., Lipsitz, S. R., Dominici, L. S., Lynce, F. C., Minami, C. A., Nakhlis, F., Waks, A. G., Warren, L. E., Eidman, N., Frazier, J., et al. (2024) · Cancer · doi:10.1002/cncr.35521 · Cited by 15 · see citing works
A Comparative Analysis of Three Large Language Models on Bruxism Knowledge
Camargo, E. S., Quadras, I. C. C., Garanhani, R. R., de Araújo, C. M., & Stuginski‐Barbosa, J. (2025) · Journal of Oral Rehabilitation · doi:10.1111/joor.13948 · Cited by 15 · see citing works
Bayram, H. M., Çelik, Z. M., & Güzeldere, H. K. B. (2025) · Nutrition and Health · doi:10.1177/02601060251329070 · Cited by 15 · see citing works
Dual-process theory and decision-making in large language models
Brady, O., Nulty, P., Zhang, L., Ward, T. E., & McGovern, D. P. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-025-00506-1 · Cited by 16 · see citing works
Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)
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Norris, M. L., Obeid, N., & Emam, K. E. (2024) · International Journal of Eating Disorders · doi:10.1002/eat.24215 · Cited by 13 · see citing works
Wyatt, K. D., Alexander, N., Hills, G. D., Liang, W. H., Kadauke, S., Volchenboum, S. L., Mian, A., & Phillips, C. (2024) · Pediatric Blood & Cancer · doi:10.1002/pbc.31143 · Cited by 13 · see citing works
Li, B., Tauseef, S., Truong, K. N., & Mariakakis, A. T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713613 · Cited by 13 · see citing works
Development and evaluation of a lightweight large language model chatbot for medication enquiry
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Ehtesham, A., Singh, A., & Kumar, S. (2025) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot65859.2025.11105280 · Cited by 13 · see citing works
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Advancing Healthcare Accessibility: Development of an AI-Driven Multimodal Chatbot
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Baskara, F. R. (2025) · Journal of Languages and Language Teaching · doi:10.33394/jollt.v13i1.9926 · Cited by 22 · see citing works
Enhanced large language models for effective screening of depression and anxiety
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Bulck, L. V., & Moons, P. (2023) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvad049 · Cited by 11 · see citing works
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AI versus the spinal surgeons in the management of controversial spinal surgery scenarios
Mehmet, S., Elmarawany, M. N., Harding, I., Bowey, A. J., Andrews, J., Chan, D., Jayasuriya, R. L., Srinivas, S., Tomlinson, J. E., Bayley, E. J., et al. (2025) · European Spine Journal · doi:10.1007/s00586-025-08825-w · Cited by 10 · see citing works
The role of artificial intelligence in gynecologic and obstetric emergencies
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General Practitioners’ Perspectives on a Pre-Consultation Chatbot for Shared Decision-Making
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Optimizing ChatGPT’s Interpretation and Reporting of Delirium Assessment Outcomes: Exploratory Study
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Withdrawn: Large Language Models in Healthcare Decision Support: A ReviewPreprint
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Artificial intelligence in academic Research: Contributor, constructivist or cheat?
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Assessing the research landscape and clinical utility of large language models: A scoping reviewPreprint
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Evaluating the performance of ChatGPT in clinical multidisciplinary treatment: a retrospective study
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Medical QA dialogue datasets in RAG systems performance evaluation and ChatGPT optimization
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Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology
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Using chatgpt to enhance student learning: a case study in a nursing curriculum
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Large Language Model-assisted EIA screening: a case study using GPT
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Evaluating chatbots in psychiatry: Rasch-based insights into clinical knowledge and reasoning
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Leveraging Virtual Reality to Enhance Diversity, Equity and Inclusion training at Google
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- Authors
- Au Yeung, J., Kraljevic, Z., Luintel, A., Balston, A., Idowu, E., Dobson, R. J., & Teo, J. T.
- Affiliations
- King's College Hospital (GB)
- St Thomas' Hospital (GB)
- King's College London (GB)
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2023-04-12
- Volume
- 5
- Pages
- 1161098-1161098
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC10130576
- OpenAlex
- W4365148488
- Cited by
- 176
- References
- 23
- Citations by year
- 2026: 21 · 2025: 61 · 2024: 59 · 2023: 34 · 2013: 1
- FWCI
- 6.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute for Health and Care Research (ACF-2021-17-002)
- King's College London
- Medical Research Council (HDR-23004, MC_PC_17214)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- large language models, chatbots, clinical decision support, ChatGPT, foresight, generative pre-trained transformer, clinical vignettes, medical question answering
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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