Abstract A063: Artificial Intelligence in Cytopathology and Histopathology of Cancer: The Evolution to Precision Pathology
Bibliographic record
Abstract
Abstract Cytopathology and histopathology are fields in diagnostic pathology that involve analysis of patient samples derived from lung, the cervix, bone marrow, breast and the thyroid, and including other organs. Ensuring accuracy in terms of sensitivity and specificity that minimize false negative and false positive rates, as well as analytic validity that demonstrates the prognostic and predictive value of the clinical samples is integral to the validity of the pathological examination and analysis of slides. Artificial intelligence, including machine learning and deep learning, are increasingly being applied to cytopathological and histopathological specimens in cancer for the diagnosis of lung nodules and bone marrow aspirates and the screening for cervical cancer through Pap smears, as well as breast cancer biopsy histological stains (Hays 2024). AI assisted methods are being applied to cytopathological and histopathological data and have shown to lead to greater sensitivity and specificity and decreased intersubject and intrasubject variability when differences result from the analysis and diagnosis of samples. The evolution toward precision pathology may be taking place whereby digital pathology is enhancing whole slide imaging and artificial intelligence methods are being incorporated into pathological workflows to improve diagnostic accuracy and mitigate variability while also assisting pathologists in decreasing their workload. This abstract will elaborate on the clinical studies and data supporting this and the artificial intelligence algorithms developed to analyze pathological samples, as well as highlight the challenges stemming from transitioning to this model and the clinical relevance and implications of “precision pathology.” Citation Format: Priya Hays. Artificial Intelligence in Cytopathology and Histopathology of Cancer: The Evolution to Precision Pathology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A063.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".