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Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in

Dipesh Niraula, Kyle C Cuneo, Ivo D Dinov

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    This study explored how artificial intelligence clinical decision support systems (AI-CDSS) impact clinicians' treatment decisions for adaptive radiotherapy. AI assistance influenced decisions, increasing agreement with AI recommendations and reducing inter-physician variability.

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    Area of Science:

    • Oncology
    • Medical Informatics
    • Radiotherapy

    Background:

    • Personalized cancer care can be enhanced by adaptive treatment strategies that dynamically adjust to individual disease progression.
    • Artificial intelligence clinical decision support systems (AI-CDSS) integrating longitudinal multi-omics data can aid clinicians in optimizing therapeutic choices.
    • The accuracy of AI-CDSS is not absolute, necessitating an investigation into the collaborative decision-making process between clinicians and AI to avoid unintended consequences.

    Approach:

    • A two-phase human-AI interaction case study was conducted on response-adaptive radiotherapy (RT) for non-small cell lung cancer (NSCLC) and hepatocellular carcinoma (HCC).
    • Clinicians evaluated retrospective patient cases, first without AI assistance (Unassisted Phase) and then with AI-CDSS support (AI-assisted Phase), considering mid-treatment dose modifications.
    • The AI-CDSS provided graphical trade-offs in tumor control versus organ-at-risk toxicity, along with optimal recommendations and uncertainty estimates. Clinician trust, confidence, and decision rationale were recorded.

    Key Points:

    • AI-assistance led to decision adjustments in 57% of NSCLC and 47% of HCC cases, with greater adjustments correlating with initial dissimilarity to AI recommendations.
    • Clinician agreement with AI recommendations positively correlated with trust in the AI system, indicating a greater likelihood of following AI suggestions when trust is high.
    • Inter-evaluator agreement increased with AI assistance, suggesting that AI-CDSS can help standardize clinical decisions and reduce inter-physician variability in adaptive RT.

    Conclusions:

    • Human-AI interaction in clinical decision-making is influenced by a complex interplay of clinician expertise, patient factors, disease characteristics, and AI model transparency and behavior.
    • Clinicians' responses to AI recommendations varied, ranging from complete disregard to critical analysis and adoption based on perceived potential for improved outcomes.
    • AI-CDSS can serve as a valuable tool in adaptive radiotherapy, but its integration requires careful consideration of clinician trust, model transparency, and the dynamic nature of human-AI collaboration.