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    This study introduces a framework to detect AI impersonation in oncology, ensuring AI-generated clinical notes maintain oncologic reasoning and procedural logic for trustworthy AI integration.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Oncology Informatics
    • Natural Language Processing

    Background:

    • Large language models (LLMs) are increasingly used in oncology for clinical documentation.
    • LLMs' fluency can mask a risk of impersonating oncologists without true oncologic reasoning.
    • Existing methods lack domain-specific checks for AI-generated oncology content.

    Purpose of the Study:

    • To present a framework for detecting domain-level role impersonation in AI-generated oncology communication.
    • To quantify semantic fidelity and procedural coherence in AI-generated clinical notes.
    • To safeguard the trustworthy integration of generative AI in oncology practice.

    Main Methods:

    • Developed a role impersonation index (RII) using semantic fidelity and procedural coherence (PC).
    • Aligned generated content with expert knowledge from ontologies (SNOMED CT, UMLS, NCIt, OncoTree).
    • Evaluated on 4,800 clinical oncology notes (human vs. AI authorship).

    Main Results:

    • The framework detects inconsistencies in tumor terminology, staging, biomarker logic, and therapeutic sequencing.
    • Achieved a 94% F1-score and 0.96 AUROC in distinguishing human from AI authorship.
    • Successfully identified impersonation without relying on superficial linguistic cues.

    Conclusions:

    • The proposed framework provides a principled safeguard against AI role impersonation in oncology.
    • Integrating knowledge-graph alignment with procedural logic scoring is novel for detecting impersonation.
    • This approach supports the reliable use of generative AI in cancer care documentation.