Application of artificial intelligence and digital tools in cancer pathology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it