ReviewJournal of anesthesia2026
Generative AI in perioperative medicine and anesthesiology: ethical integration, educational innovation, and the future of clinical professionalism.
Review in Journal of anesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) is rapidly transforming perioperative medicine, particularly anesthesiology, by enabling novel applications, such as real-time data synthesis, individualized risk prediction, and automated documentation. These capabilities enhance clinical decision-making, patient communication, and workflow efficiency in the operating room. In education, generative AI offers immersive simulations and tailored learning experiences that improve both technical skills and professional judgment. However, overreliance without critical appraisal may compromise patient safety and humanistic care. This paper introduces a novel professionalism framework for anesthesiology in the AI era, comprising three pillars: critical AI literacy, human-centered care, and digital accountability. The model supports resident training, certification, and lifelong learning by integrating AI competencies with ethical awareness and reflective practice. By encouraging anesthesiologists to critically engage with AI tools, the framework ensures safe, effective, and compassionate perioperative care.
Indexed as
Identifiers
40931244What OpenQuestion holds
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.