Integration of AI in Barrett’s Esophagus Clinical Practice: A New Way Forward
Bibliographic record
Abstract
Barrett’s esophagus is the only known precursor for esophageal adenocarcinoma. The stepwise progression of nondysplastic Barrett’s to low-grade dysplasia, high-grade dysplasia, intramucosal carcinoma, and invasive cancer provides an opportunity for endoscopic surveillance, with the goal to detect and effectively treat early neoplastic changes endoscopically, in order to avoid surgery and chemoradiation. Multiple limitations in the current endoscopic surveillance practice have led to missed early neoplasia and therefore reduced the effectiveness of the current surveillance practice. Artificial intelligence (AI)-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current endoscopic surveillance practice for Barrett’s esophagus, its limitations, the potential role of AI to improve these limitations, and our suggested framework to integrate AI into Barrett’s clinical 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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| 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".