ArticleJAMA surgery2026
Expectations vs Reality of an Intraoperative Artificial Intelligence Intervention.
Article in JAMA surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Surgical scene understanding and the structural validation gap in an industry-led AI ecosystem.NPJ digital medicine · 2026Article
- A Conceptual Model for Embedding Automated Assessments in Graduate Medical Education.Journal of graduate medical education · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
Importance: Having significant gaps between the expectations and reality of artificial intelligence-based programs can be a major barrier to successful implementation. This is the first multisite implementation assessment of gaps between surgeon expectations and real-world effects of the Operating Room Black Box, a novel intervention that leverages artificial intelligence to improve surgical outcomes. Objective: To identify barriers and facilitators to implementing artificial intelligence-based interventions that improve intra- and postoperative care. Design, Setting, and Participants: This qualitative study was conducted at 3 large academic centers via semistructured interviews with surgeons and implementation leaders of the AI intervention to identify areas where expectations of the technology misaligned with their experiences. Thirty surgeons and 17 implementation leaders from 3 centers that implemented the AI intervention were interviewed. Data were collected and analyzed between 2021 and 2024. Exposure: Implementation of the AI intervention. Main Outcomes and Measures: The primary outcome was areas of misalignment between participant expectations of the AI intervention technology and actual program deliverables. Results: Of 30 surgeons and 17 implementation leaders interviewed, most surgeons (17 [57%]) were between the ages of 35 and 50 years, and implementation leaders were older, typically between 51 and 80 years old (6 [35%]). Eight surgeons (27%) and 4 implementation leaders (24%) were female. Most surgeons (17 [57%]) had neutral views of the technology, 11 (37%) expressed positive views, and 2 (7%) had negative views. Interviewees identified the following 4 major themes that highlighted misalignment between user expectations and the experience of using the technology: (1) the artificial intelligence model needed considerable additional training to be usable; (2) accessing data on surgical cases was difficult and time consuming; (3) the program showed limited ability to predict postoperative complications; and (4) the program generated few academic deliverables. Conclusions and Relevance: Per the results of this multisite qualitative study, successfully implementing interventions based on artificial intelligence may require deliberate efforts to minimize gaps between what surgeons expect from the interventions and what they can deliver. Our evaluation of this study's AI intervention offers lessons for addressing this critical barrier to implementation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.