ArticleCureus2025
Evaluating the Performance and Safety of Large Language Models in Generating Type 2 Diabetes Mellitus Management Plans: A Comparative Study With Physicians Using Real Patient Records.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- From risk classification to clinical action: a prespecified paired pilot benchmark of public large language model interfaces for diabetes-related foot ulcer prevention.Frontiers in endocrinology · 2026Article
- Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background The integration of large language models (LLMs) such as GPT-4 into healthcare presents potential benefits and challenges. While LLMs show promise in applications ranging from scientific writing to personalized medicine, their practical utility and safety in clinical settings remain under scrutiny. Concerns about accuracy, ethical considerations, and bias necessitate rigorous evaluation of these technologies against established medical standards. Methods This study involved a comparative analysis using anonymized patient records from a healthcare setting in the state of West Bengal, India. Management plans for 50 patients with type 2 diabetes mellitus were generated by GPT-4 and three physicians, who were blinded to each other's responses. These plans were evaluated against a reference management plan based on American Diabetes Society guidelines. Completeness, necessity, and dosage accuracy were quantified and a Prescribing Error Score was devised to assess the quality of the generated management plans. The safety of the management plans generated by GPT-4 was also assessed. Results Results indicated that physicians' management plans had fewer missing medications compared to those generated by GPT-4 (p=0.008). However, GPT-4-generated management plans included fewer unnecessary medications (p=0.003). No significant difference was observed in the accuracy of drug dosages (p=0.975). The overall error scores were comparable between physicians and GPT-4 (p=0.301). Safety issues were noted in 16% of the plans generated by GPT-4, highlighting potential risks associated with AI-generated management plans. Conclusion The study demonstrates that while GPT-4 can effectively reduce unnecessary drug prescriptions, it does not yet match the performance of physicians in terms of plan completeness. The findings support the use of LLMs as supplementary tools in healthcare, highlighting the need for enhanced algorithms and continuous human oversight to ensure the efficacy and safety of artificial intelligence in clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.