Large language models vs thrombosis experts: a comparative study on patient education and clinical decision-making in venous thromboembolism
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) excel in patient education for thrombosis and hemostasis, outperforming experts. LLMs also match or exceed expert performance in clinical decision-making, with responses indistinguishable from human experts.
Area Of Science
- Medical Artificial Intelligence
- Clinical Decision Support Systems
- Biomedical Informatics
Background
- Large language models (LLMs) show promise in medicine, but their utility in thrombosis and hemostasis remains unexamined.
- The application of LLMs for patient education and clinical decision-making in this specialized field is unexplored.
Purpose Of The Study
- To compare the quality of responses generated by LLMs and human experts in thrombosis and hemostasis.
- To evaluate clinicians' ability to differentiate between LLM-generated and expert-authored content.
Main Methods
- Three LLMs (Le Chat Pixtral Large, DeepSeek-R1, ChatGPT-4.5) and three thrombosis experts responded to patient education and clinical decision-making queries.
- Thirty-seven physicians rated response adequacy and estimated the origin (LLM vs. human) of the answers.
- Statistical analyses included t-tests, Wilcoxon tests, and Spearman correlations, with Bonferroni correction for P values.
Main Results
- LLMs significantly outperformed experts in patient education responses, with higher mean adequacy scores across all tested models.
- In clinical decision-making, DeepSeek-R1 surpassed experts, while Le Chat Pixtral Large and ChatGPT-4.5 performed comparably to experts.
- Physicians could not reliably distinguish between expert and LLM-generated responses.
Conclusions
- LLMs demonstrate superior performance in patient education for venous thromboembolism and match or exceed expert capabilities in clinical decision-making.
- LLM-generated responses were indistinguishable from those of human experts, highlighting their potential.
- Despite existing barriers, LLMs offer significant potential to enhance clinical management and patient education in thrombosis and hemostasis.
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