Head and neck pathology · 2026
Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.
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Tavares Tiffany, Sangalli Linda, Menon Reshma S, Miller Craig S, Hochman Guy
Аннотация
CONTEXT: This paper examines how artificial intelligence (AI) is reshaping diagnostic decision-making in academic and clinical head and neck pathology (HNP) and argues for the redefinition of post-doctoral education in the age of AI.
PERSPECTIVE: While AI promises enhanced diagnostic accuracy, reproducibility, and efficiency, its integration also amplifies human cognitive biases and introduces new forms of error. Here we build on the 2019 Accreditation Council for Graduate Medical Education (ACGME) pathology milestones to provide an educational framework that prepares clinicians to engage critically and responsibly with AI-assisted diagnostics. We emphasize the importance of integration of insights from decision science, cognitive psychology, and medical education with recent applications of AI in HNP, including histopathology, radiology, and multi-omics modeling. By mapping how human reasoning processes interact with algorithmic systems, we identify cognitive, ethical, and institutional vulnerabilities that arise in hybrid human-machine decision environments. Our analysis highlights that AI does not eliminate diagnostic uncertainty but redistributes it. Automation bias, overconfidence, and anchoring may distort reasoning when clinicians fail to question algorithmic outputs. To address these challenges, we outline a model of healthcare-optimization training, centered on three core skills: AI literacy, diagnostic skepticism, and ethical transparency. As such, reframing decision-making education in HNP requires moving beyond technical training toward reflective, ethically grounded practice.
RECCOMENDATION: Embedding AI literacy and critical reasoning within competency-based curricula and institutional governance can ensure that AI enhances rather than replaces human judgment-fostering safer, more equitable, and patient-centered diagnostic care.
PERSPECTIVE: While AI promises enhanced diagnostic accuracy, reproducibility, and efficiency, its integration also amplifies human cognitive biases and introduces new forms of error. Here we build on the 2019 Accreditation Council for Graduate Medical Education (ACGME) pathology milestones to provide an educational framework that prepares clinicians to engage critically and responsibly with AI-assisted diagnostics. We emphasize the importance of integration of insights from decision science, cognitive psychology, and medical education with recent applications of AI in HNP, including histopathology, radiology, and multi-omics modeling. By mapping how human reasoning processes interact with algorithmic systems, we identify cognitive, ethical, and institutional vulnerabilities that arise in hybrid human-machine decision environments. Our analysis highlights that AI does not eliminate diagnostic uncertainty but redistributes it. Automation bias, overconfidence, and anchoring may distort reasoning when clinicians fail to question algorithmic outputs. To address these challenges, we outline a model of healthcare-optimization training, centered on three core skills: AI literacy, diagnostic skepticism, and ethical transparency. As such, reframing decision-making education in HNP requires moving beyond technical training toward reflective, ethically grounded practice.
RECCOMENDATION: Embedding AI literacy and critical reasoning within competency-based curricula and institutional governance can ensure that AI enhances rather than replaces human judgment-fostering safer, more equitable, and patient-centered diagnostic care.