Optical Polyp Diagnosis in the Era of Artificial Intelligence
Bibliographic record
Abstract
The development of new image enhancement modalities and improved endoscopic imaging quality has not led to increased adoption of resect-and-discard in routine practice. Studies have shown that endoscopists have the capacity to achieve quality thresholds to perform optical diagnosis; however, this has not led to acceptance of optical diagnosis as a replacement for pathology for diminutive (1-5 mm) polyps. In recent years, artificial intelligence (AI)-based computer-assisted characterization of diminutive polyps has recently emerged as a strategy that could potentially represent a breakthrough technology to enable widespread adoption of resect-and-discard. Recent evidence suggests that pathology-based diagnosis is suboptimal, as polyp nonretrieval, fragmentation, sectioning errors, incorrect diagnosis as "normal mucosa," and interpathologist variability limit the efficacy of pathology for the diagnosis of 1-5 mm polyps. New paradigms in performing polyp diagnosis with or without AI have emerged to compete with pathology in terms of efficacy. Strategies, such as autonomous AI, AI-assisted human diagnosis, AI-unassisted human diagnosis, and combined strategies have been proposed as potential paradigms for resect-and-discard, although further research is still required to determine the optimal strategy. Implementation studies with high patient acceptance, where polyps are truly being discarded without histologic diagnosis, are paving the way toward normalizing resect-and-discard in routine clinical practice. Ultimately the largest challenges for computer-assisted characterization remain liability perceptions from endoscopists. The potential benefits of AI-based resect-and-discard are many, with very little potential harm. Real-world implementation studies are therefore required to pave the way for the acceptability of such strategies in routine practice.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".