Evidence map›Paper›PMID 40931244›Full record

ReviewJournal of anesthesia2026

Generative AI in perioperative medicine and anesthesiology: ethical integration, educational innovation, and the future of clinical professionalism.

Nobuyasu Komasawa

Abstract readReview
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nobuyasu KomasawaCommunity Medicine Education Promotion Office, Faculty of Medicine, Kagawa University Ikenobe, 1750-1, Miki-Cho, Kagawa, 761-0793, Japan. komasawa.nobuyasu@kagawa-u.ac.jp.ORCID http://orcid.org/0000-0002-1703-2813

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

AnesthesiologyArtificial IntelligencePerioperative MedicineProfessionalismClinical CompetenceHumansAnesthetic educationClinical decision supportGenerative artificial intelligencePerioperative careProfessionalism in medicine

Identifiers

PMID40931244

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.