Modernizing pathology and oncology education: integrating genomics, artificial intelligence, and clinical relevance into medical training
Bibliographic record
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 demand exposure to digital pathology and critical appraisal of algorithmic outputs, including AI‑supported 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 LCME and 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".