ArticleCureus2026
Artificial Intelligence in Perioperative and Pain Care: Knowledge, Attitudes, and Adoption Barriers Among Anesthesiologists.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background The integration of artificial intelligence (AI) in anesthesiology holds immense potential to revolutionize perioperative care. Despite the growing interest in AI, limited research exists on anesthesiologists' knowledge, attitudes, and practical exposure to AI-based tools. This study aimed to assess the knowledge, attitudes, and awareness of AI applications in perioperative care of anesthesiologists with AI applications in perioperative care, spanning preoperative, intraoperative, and postoperative domains. Methodology A structured questionnaire was distributed among anesthesiologists across six major cities of Gujarat, India: Ahmedabad, Vadodara, Surat, Rajkot, Jamnagar, and Bhavnagar. The survey included demographic details, awareness of AI applications, attitudes toward AI adoption, and perceived challenges. Descriptive and inferential statistics were used to analyze the data. Results Among 385 respondents, 94.0% (n = 362) had heard of AI in healthcare, but only 63.6% (n = 245) were aware of its role in anesthesia. Knowledge assessment categorized 21.3% (n = 82) as having good knowledge, 70.9% (n = 273) as average, and 7.8% (n = 30) as poor. While 90.4% (n = 348) agreed that AI training could enhance adoption, 84.4% (n = 325) believed AI would ease their workload. However, 9.6% (n = 37) were concerned that AI-equipped doctors might replace those without AI expertise. The most cited barriers to AI adoption were lack of knowledge (84.4%, n = 325), legal concerns (27.3%, n = 105), and limited validation studies (16.9%, n = 65). Despite these challenges, 94.0% (n = 362) expressed willingness to read AI literature, and 93.2% (n = 359) reported improved knowledge post-survey. A statistically significant association was observed between years of work experience and AI knowledge levels (p < 0.01). Conclusions This study highlights a growing interest in AI among anesthesiologists but also underscores substantial knowledge gaps and implementation challenges. While attitudes toward AI are largely positive, concerns regarding training, validation, and medico-legal implications must be addressed. Strengthening AI education, fostering interdisciplinary collaboration, and ensuring ethical integration into clinical practice will be crucial for optimizing AI's role in anesthesia. Future research should focus on validating AI tools and assessing their clinical impact.
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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.