Application of artificial intelligence and digital tools in cancer pathology
Bibliographic record
Abstract
Artificial intelligence (AI) is on the verge of reshaping cancer diagnostics through integration into digital pathology workflows. Despite the progression of AI towards real-world deployment, challenges in interpretability, validation, and clinical integration persist. AI models support the interpretation of stains including haematoxylin and eosin, enabling tumour classification, grading, and biomarker quantification, with clinical applications for targets such as HER2 and PD-L1. In addition, AI models enable the quantification of subtle microscopic patterns with prognostic and predictive values across tumour types. Herein, we provide an overview of the applications of AI in pathology and address emerging regulatory and ethical considerations. We also discuss the disparities in adoption across care settings and emphasise the importance of validation, human oversight, and post-deployment monitoring for the responsible implementation of AI in pathology-driven workflows. Furthermore, we highlight the technical advancements driving these developments, particularly the transition from hand-crafted machine learning workflows to deep learning, self-supervised learning for foundation models, multimodal models, and agentic AI.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".