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Agreement Between AI Language Models and BOOM Chondrosarcoma Consensus Statements: A Comparative Study.

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    Expert statements from the Birmingham Orthopaedic Oncology Meeting (BOOM) on chondrosarcomas were compared to AI models. AI responses showed a significant relationship but differed from human expert consensus, especially with lower evidence levels.

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    Area of Science:

    • Orthopaedic Oncology
    • Artificial Intelligence in Medicine

    Background:

    • Artificial intelligence (AI) offers potential solutions for complex medical problems.
    • The Birmingham Orthopaedic Oncology Meeting (BOOM) convened experts to establish consensus on chondrosarcoma management.

    Purpose of the Study:

    • To compare the reliability of expert consensus statements from the BOOM with AI models (ChatGPT-4 and DeepseekR1).
    • To evaluate AI performance in interpreting evidence levels and consensus strength for chondrosarcoma treatment questions.

    Main Methods:

    • 21 consensus questions and statements on chondrosarcomas from the BOOM were extracted.
    • AI models (ChatGPT-4, DeepseekR1) interpreted these statements using a Likert scale (strongly disagree to strongly agree).
    • Responses were analyzed based on evidence level and consensus strength determined by BOOM participants.

    Main Results:

    • BOOM participants achieved strong consensus on 19 out of 21 questions.
    • ChatGPT-4 and DeepseekR1 agreed on a 'disagree' response for one question where BOOM had low-to-moderate evidence but strong consensus.
    • A significant correlation was observed between ChatGPT-4 and DeepseekR1 responses; both AI models favored higher evidence levels, unlike BOOM experts.

    Conclusions:

    • AI models demonstrate a significant relationship in their responses but do not fully replicate expert clinical judgment.
    • Expert consensus, integrating clinical experience and literature, appears more robust than AI interpretation, particularly with varying evidence quality.
    • Further research is needed to refine AI's role in supporting clinical decision-making for complex oncological conditions like chondrosarcoma.