The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Abstract
Chatbots are computer programs with which one can have a conversation. In this article, the authors describe how the GPT-4 chatbot, which has been given a general education, could affect the practice of medicine.
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In the citing paper
“Large language models (LLMs), with their superior contextual semantic understanding, offer a promising avenue to overcome these limitations [8, 9].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [8]
In the citing paper
“Previous research has documented LLM hallucination in various contexts, including medical information [8], legal reasoning [9], and general factual queries [10].”
From Compound Deception in Elite Peer Review: A Failure Mode Taxonomy of 100 Fabricated Citations at NeurIPS 2025 (2026) · cites this paper as [8]
In the citing paper
“This is in contrast to prior work that claims LMs approach human performance, even for complex tasks (Ding et al., 2022; Nori et al., 2023; Lee et al., 2023) even though the task of writing biographies is fairly easy.”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Lee et al., 2023
In the citing paper
“This is in contrast to prior work that claims LMs approach human performance, even for complex tasks (Ding et al., 2022; Nori et al., 2023; Lee et al., 2023) even though the task of writing biographies is fairly easy.”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Lee et al., 2023
In the citing paper
“Studies evaluating GPT-4 and Claude on medical board examinations report accuracy rates of 80-90% [3,6]”
From Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware (2026) · cites this paper as [3]
In the citing paper
“Large Language Models (LLMs) demonstrating remarkable capabilities in medical knowledge synthesis, diagnostic reasoning, and clinical documentation [1-3].”
From Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware (2026) · cites this paper as [3]
In the citing paper
“Most published medical AI benchmarks employ enterprise GPUs (NVIDIA A100, H100) or cloud API access, reporting neither hardware specifications nor inference costs [2,3,7].”
From Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware (2026) · cites this paper as [3]
In the citing paper
“While these models offer powerful foundational capabilities, adapting them to specific tasks and datasets without losing robustness is a significant hurdle, often resulting in significant variability in accuracy across different settings.7,9,10”
In the citing paper
“Despite their ability to create novel and customized content, these models are prone to hallucinations7 and can produce factually incorrect yet plausible outputs that may mislead users.”
In the citing paper
“If generative AI in health care is used for diagnostics, personalized medicine, or clinical assistance, AI hallucination can be extremely dangerous and may even harm patients’ lives [83].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [83]
In the citing paper
“However, their clinical deployment carries substantial limitations, including the generation of inaccurate or fabricated outputs (commonly termed “hallucinations”), variable output quality, and the absence of transparent reasoning pathways [4].”
In the citing paper
“Without full knowledge of the intervention being applied, it is difficult to interpret LLM performance.9,17,156”
From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [9]
Citations
Cites
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Deep Learning Applications in Medical Image Analysis
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A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning
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The US Medical Licensing Examination
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Research works printed but not linked: 1
- Corbelle JG Bugarín-Diz A Alonso-Moral J Taboada J. Dealing with hallucination and omission in neural Natural Language Generation: a use case on meteorology. In: Proceedings and Abstracts of the 15th International Conference on Natural Language Generation July 18–22 2022. Waterville ME: Arria 2022.
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Metadata
- Authors
- Lee, P., Bubeck, S., & Petro, J.
- Affiliations
- Microsoft (United States) (US)
- From Microsoft Research, Redmond, WA (P.L., S.B.); and Nuance Communications, Burlington, MA (J.P.).
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2023-03-30
- Volume
- 388
- Issue
- 13
- Pages
- 1233-1239
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W4361289889
- Semantic Scholar
- 72b74bcff8fd76eff6789111a7ce5d0d6c5ac4db
- Cited by
- 1,738
- References
- 6
- Citations by year
- 2026: 405 · 2025: 419 · 2024: 542 · 2023: 370
- FWCI
- 69.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- GPT-4, large language models, AI chatbots, medical practice, clinical decision support, artificial intelligence in healthcare
- MeSH terms
- Artificial Intelligence, Humans, Medicine, Risk Assessment
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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