July 17, 2024Journal of the American Medical Informatics Association

The potential and pitfalls of using a large language model such as ChatGPT, GPT-4, or LLaMA as a clinical assistant

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Authors

JZJingqing ZhangHealth Data Research UK KSKai SunNanfang Hospital AJAkshay V. JagadeeshHarvard University

Discussion

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Implication

Randomized trial assesses diagnostic performance of large language models in patient identification, suggesting significant implications for healthcare practice.

Key Points

GPT-4 demonstrates superior patient identification performance compared to traditional models—F1-score exceeds 85%.
In identifying Chronic Obstructive Pulmonary Disease, Chronic Kidney Disease, and Primary Biliary Cirrhosis, GPT-4 outperforms disease-specific algorithms by 4.23% on F1-score.
Assessment using gold-labeled electronic health records focused on enhancing clinical decision-making for various prevalent diseases and conditions observed significant results in diagnostics and precision indicating potential utility in healthcare settings. Overall, highlights the pressing need for better training datasets and design improvements for large language models in clinical applications.

Cite This Study

Zhang et al. (2024) studied this question.

synapsesocial.com/papers/68e5fef1b6db643587592cdd https://doi.org/10.1093/jamia/ocae184

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July 17, 2024Journal of the American Medical Informatics Association

The potential and pitfalls of using a large language model such as ChatGPT, GPT-4, or LLaMA as a clinical assistant

View Full Paper

Ask AI

Bookmark

Authors

JZJingqing ZhangHealth Data Research UK KSKai SunNanfang Hospital AJAkshay V. JagadeeshHarvard University

Discussion

Member takes

Implication

Randomized trial assesses diagnostic performance of large language models in patient identification, suggesting significant implications for healthcare practice.

Key Points

GPT-4 demonstrates superior patient identification performance compared to traditional models—F1-score exceeds 85%.
In identifying Chronic Obstructive Pulmonary Disease, Chronic Kidney Disease, and Primary Biliary Cirrhosis, GPT-4 outperforms disease-specific algorithms by 4.23% on F1-score.
Assessment using gold-labeled electronic health records focused on enhancing clinical decision-making for various prevalent diseases and conditions observed significant results in diagnostics and precision indicating potential utility in healthcare settings. Overall, highlights the pressing need for better training datasets and design improvements for large language models in clinical applications.

Cite This Study

Zhang et al. (2024) studied this question.

synapsesocial.com/papers/68e5fef1b6db643587592cdd https://doi.org/10.1093/jamia/ocae184

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