The integration of large language models (LLMs) into healthcare and the daily lives of many people has raised critical questions regarding the accuracy of AI-generated medical information. While early enthusiasm has emphasized their potential to improve access to knowledge and reduce clinician workload, emerging evidence indicates that LLM performance is highly variable and context dependent. A 2025 systematic review comparing LLM responses to those of healthcare professionals found that, although many studies favored LLM-generated answers, results were inconsistent and often mixed, underscoring concerns about reliability and safety in real-world use.1 Large-scale meta-analyses similarly demonstrate that LLM accuracy varies substantially by task type, with higher performance on structured, objective questions and lower reliability in open-ended or clinically nuanced scenarios.
Although advanced models such as GPT-4 variants can approach expert-level performance on standardized examinations, human clinicians remain more reliable in tasks requiring precise diagnostic reasoning. LLMs underperform in identifying the single most likely diagnosis or generating accurate, prioritized differential diagnoses.2 Much of the existing literature overestimates LLM capability by relying on these exam-style benchmarks rather than real-world interactions. Accuracy rates exceeding 80% on licensing-style questions do not necessarily translate into safe or actionable medical guidance in practice.3
This gap between model capability and real-world use is highlighted in a randomized study published in Nature Medicine. When clinical scenarios were provided directly to LLMs (with each model assessing each scenario 60 times), models identified at least one relevant condition in over 90% of responses. However, participants using the same models identified relevant conditions in fewer than 34.5% of cases.4 This discrepancy demonstrates that accuracy is not solely a function of model knowledge, but also depends on user interaction, prompt quality, and interpretation of model outputs. In practice, even high-performing models may fail to improve—and may even degrade—clinical reasoning when used by non-experts.
Beyond issues with accuracy as a result of interpretation, AI large language models may also generate or accept incorrect medical information. A large benchmarking study found that LLMs accepted fabricated medical content in approximately 31.7% of cases, with even higher error rates when misinformation was embedded in realistic clinical narratives.5 These findings reflect a fundamental limitation: LLMs lack robust mechanisms for distinguishing truth from plausibly structured falsehoods, and this flaw is especially pronounced in complex clinical contexts.
Finally, model performance remains inconsistent across datasets and clinical environments. While larger and domain-adapted models generally achieve higher accuracy, their performance does not generalize reliably. For example, GPT-4 demonstrates strong performance on the MedQA dataset, which reflects U.S. licensing examinations, but performs significantly worse on datasets based on different clinical guidelines, such as MedMCQA.6 This variability highlights the dependence of LLM accuracy on training data distribution and clinical context.
Taken together, current evidence suggests that AI large language models can provide accurate medical information under controlled conditions, particularly for structured queries, but their accuracy degrades in complex, interactive, and real-world settings. Accuracy is therefore not a fixed property of these systems, but a function of task design, user interaction, and contextual complexity. While LLMs hold promise as supportive tools, their use in clinical or patient-facing contexts requires careful oversight. Future research should prioritize standardized evaluation frameworks, real-world validation, and strategies to mitigate hallucination and misinformation to ensure safe and effective deployment.
References
1. Jacobs MMG, Oosterhoff JHF, Agricola R, van der Weegen W. Large language models versus healthcare professionals in providing medical information to patient questions: A systematic review. Int J Med Inform. 2026;209:106250. doi:10.1016/j.ijmedinf.2025.106250
2. Wang L, Li J, Zhuang B, et al. Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis. J Med Internet Res. 2025;27:e64486. Published 2025 Apr 30. doi:10.2196/64486
3. Jaleel A, Aziz U, Farid G, et al. Evaluating the Potential and Accuracy of ChatGPT-3.5 and 4.0 in Medical Licensing and In-Training Examinations: Systematic Review and Meta-Analysis. JMIR Med Educ. 2025;11:e68070. Published 2025 Sep 19. doi:10.2196/68070
4. Bean AM, Payne RE, Parsons G, et al. Reliability of LLMs as medical assistants for the general public: a randomized preregistered study. Nat Med. 2026;32(2):609-615. doi:10.1038/s41591-025-04074-y
5. Omar M, Sorin V, Wieler LH, et al. Mapping the susceptibility of large language models to medical misinformation across clinical notes and social media: a cross-sectional benchmarking analysis. Lancet Digit Health. 2026;8(1):100949. doi:10.1016/j.landig.2025.100949
6. Tofeeq K, Naseer A, Wali A. Large language models in healthcare: a systematic evaluation on medical Q/A datasets. Health Inf Sci Syst. 2025;14(1):2. Published 2025 Nov 21. doi:10.1007/s13755-025-00397-9