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Evaluating ChatGPT as an Adjunct for Radiologic Decision-Making.

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    Summary
    This summary is machine-generated.

    Large language models like ChatGPT show promise for clinical decision support in radiology. This study found ChatGPT feasible for identifying appropriate imaging services for breast cancer screening and breast pain.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Radiology Decision Support Systems

    Background:

    • Large language models (LLMs), such as ChatGPT, demonstrate advanced capabilities in specialized applications.
    • There is a lack of research evaluating the efficacy of LLMs for clinical decision support, particularly in radiology.

    Approach:

    • ChatGPT's recommendations for breast cancer screening and breast pain imaging were compared against the American College of Radiology (ACR) Appropriateness Criteria.
    • Two prompt formats were used: open-ended (OE) for a single recommendation and select all that apply (SATA) for multiple modalities.
    • Performance was scored based on adherence to ACR guidelines.

    Key Points:

    • For breast cancer screening, ChatGPT achieved an average OE score of 1.83/2 and a SATA accuracy of 88.9%.
    • For breast pain, ChatGPT achieved an average OE score of 1.125/2 and a SATA accuracy of 58.3%.

    Conclusions:

    • The findings suggest ChatGPT is a feasible tool for radiologic decision-making.
    • Its use could potentially enhance clinical workflows and promote responsible utilization of radiology services.