A136 EXPLORING ENDOSCOPIST PERCEPTIONS OF ARTIFICIAL INTELLIGENCE-AIDED COLONOSCOPY: A QUALITATIVE ANALYSIS
Bibliographic record
Abstract
Abstract Background Artificial intelligence (AI) is gaining recognition as a promising adjunct in healthcare including gastrointestinal endoscopy. Randomized controlled trials have demonstrated improved polyp detection with computer-aided detection (CADe) systems in colonoscopy. Despite the foreseeable translation of CADe systems into the endoscopy suite, no study to date has explored how introducing this novel technology influences endoscopist perceptions and cognitive processes. Aims To explore how AI assistance impacts endoscopists’ perceptions of and cognitive processes during colonoscopy. Methods Faculty in gastroenterology and general surgery at the University Health Network (Toronto, Canada) were interviewed in April 2023 to explore their baseline perceptions of AI before the planned installation of the Medtronic GI GeniusTM Intelligent Endoscopy Module, an AI polyp detection tool, at Toronto Western Hospital in May 2023. After performing a minimum of 10 colonoscopies using GI GeniusTM, participants were re-interviewed to discuss their perceptions of AI-assisted colonoscopy and how it influenced their cognition during the procedure. Analysis was informed by constructivist grounded theory, whereby interview data were transcribed and coded iteratively using constant comparison to generate themes. Results In this interim constant comparative analysis, 9 participants (6 gastroenterologists and 3 general surgeons) were interviewed to generate 16 interview transcripts (9 pre-exposure and 7 post-exposure; 1 interview pending, 1 participant on leave). Participants held the view that: (1) AI will inevitably become the standard of care in colonoscopy, where clinicians and AI function symbiotically and in partnership; (2) CADe systems may facilitate standardization of practice and reduce inter-endoscopist variability by improving detection of specific types of lesions (e.g., sessile serrated polyps); (3) CADe assistance provides a “second set of eyes” that interacts with, but does not replace, endoscopists’ cognitive processes; and (4) trainees should gain familiarity with AI systems, despite mixed opinions regarding AI’s specific role in training. Conclusions We found that study participants conceptualize AI as an essential component of colonoscopy practice and training, with endoscopist thought processes and CADe systems appearing to function symbiotically. Elucidating the specific ways in which AI and endoscopist cognition interact will facilitate the future refinement of these tools. Funding Agencies None
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".