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.

Background: At present, there are very limited studies evaluating autonomous artificial intelligence (AI) agents in oral oncology or healthcare education, and no studies have directly compared traditional frontier large language models (LLMs) with autonomous AI agents in the field of pediatric oral oncologic pathology. This study evaluated the performance of AI LLMs and an autonomous AI scientific agent in generating engaging and accessible patient information sheets for common pediatric oral pathologic conditions and rare head and neck tumors. The development of high-quality patient education materials is particularly important in pediatric pathology because parents must often navigate complex and emotionally sensitive diagnoses, including rare tumors and developmental lesions, for which accessible, patient-friendly educational resources are frequently unavailable. AI-generated patient information sheets therefore represent a potential strategy to improve communication, understanding, and shared decision-making for families facing these uncommon conditions. Methods: AI-generated patient information sheets from popular frontier chatbots on various pediatric pathological conditions and rare tumors were evaluated by five platforms (Eli5a v2.0, ChatGPT-5.4, Claude Sonnet 4.6, Perplexity and Doximity), in a blinded fashion, by three expert evaluators using the Global Quality Score (GQS), DISCERN score, understandability score and actionability score using PEMAT, and the Flesch-Kincaid Grade Level. Because the same 20 conditions were assessed on every platform, observations were paired; platforms were compared using Friedman tests with paired Wilcoxon signed-rank post hoc tests and Holm correction, and interrater reliability was quantified using an absolute-agreement intraclass correlation coefficient (ICC). Results: Platform differences were significant for all five outcomes (all p < 0.001). Claude Sonnet 4.6 obtained the best overall information quality (GQS 4.25 ± 0.39), scoring significantly higher than all other platforms, while Perplexity, Eli5a v2.0 and ChatGPT-5.4 performed similarly to one another and better than Doximity. For information reliability, Doximity (DISCERN 70.50 ± 3.20) and Claude Sonnet 4.6 (70.25 ± 3.02) performed best, while Eli5a v2.0 recorded the lowest DISCERN score (54.50 ± 4.26). Eli5a v2.0 obtained the best results for patient-centered communication, with the highest understandability (PEMAT-U 95.05 ± 1.23) and readability (FKGL 5.45 ± 0.51) and an actionability score among the highest of the five platforms (PEMAT-A 89.00 ± 2.62, not significantly different from Perplexity or Doximity). Interrater absolute agreement for GQS was poor to moderate (ICC(2,1) = 0.236; ICC(2,3) = 0.481). Conclusion: No single platform was superior across all evaluated domains. Claude Sonnet 4.6 led in overall quality whereas Eli5a v2.0 achieved the highest understandability and the most appropriatereading grade level. Eli5a’s actionability was high but did not differ significantly from Perplexity or Doximity. These findings indicate a trade-off between information quality and reliability on one hand and lay accessibility on the other. Platform selection should therefore be matched to the communication task, and all AI-generated patient materials require clinician review before use.