ReviewHead and neck pathology2026
Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.
Review in Head and neck pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Application of digital simulations for clinical assessment and documentation of temporomandibular disorders: a retrospective study.Oral surgery, oral medicine, oral pathology and oral radiology · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.