Abstract
Abstract
Pathology and oncology education are at an inflection point. Beyond abbreviated preclinical blocks, the central problem is pedagogical misalignment with learners who expect relevance, interactivity, and clinical application. We advocate a shift from content delivery to concept integration anchored in clinical reasoning and data literacy. In oncology, trainees must learn to interpret next‑generation sequencing and biomarker profiles, participate in molecular tumor boards, sequence precision therapies, manage toxicities, and incorporate patient‑reported outcomes—competencies rarely taught in a structured way. The digitization of histopathology and the integration of artificial intelligence (AI) demand exposure to digital pathology and critical appraisal of algorithmic outputs, including AI-supported immunohistochemistry (IHC) quantification, variant classification, and methylation‑based classifiers. Large language models may enhance self‑directed learning but require faculty oversight, instruction in appraisal and ethics, and safeguards against inaccuracy and overconfidence. Operationalizing these reforms requires institutional commitment; curriculum redesign that integrates pathology, oncology, genomics, and decision making; and expanded residency time to acquire competencies in informatics and AI (machine learning, deep learning, supervised and unsupervised methods, and validation). Faculty development; adoption of digital platforms and virtual microscopy; competency‑based assessment; and collaboration with computer scientists, bioinformaticians, and ethicists are essential. Implementation barriers—including limited faculty time, resource constraints, and accreditation requirements—can be mitigated by pilot programs; strategic partnerships; phased integration; and attention to transparency, equity, and accountability. Absent deliberate reform within Liaison Committee on Medical Education (LCME) and Accreditation Council for Graduate Medical Education (ACGME) frameworks that currently do not mandate genomics or AI literacy, future physicians will enter practice unprepared for precision medicine. Modernizing curricula to meet the genomics and AI era is therefore urgent.