{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:05:48Z","timestamp":1789646748040,"version":"4.0.0"},"reference-count":25,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims:<\/jats:title>\n                    <jats:p>Perioperative anaesthesia is progressively integrating decision support systems based on artificial intelligence (AI). Safe AI use requires not only technical validation but also ethical preparedness and trust to uphold patient autonomy, informed consent, and clinician oversight. This study evaluated ethical readiness and trust towards AI integration among anaesthesia professionals using a cross-sectional survey.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Anaesthesia professionals across Indian government, private, and corporate hospitals completed a structured, content-validated, and pilot-tested self-administered questionnaire assessing five domains. AI Ethical Trust and Readiness Index (AETRI) was derived as a composite score. Group differences, associations, and predictors were analysed using Mann\u2013Whitney U\/Kruskal\u2013Wallis tests, Pearson's correlation, and multiple linear regression, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      A total of 255 anaesthesia professionals participated, including 44.7% junior residents, 36.9% senior residents, 11.0% consultants, and 7.4% faculty. Most (69.4%) worked in government hospitals. The mean \u00b1 standard deviation domain scores were: Knowledge (K) 0.73 \u00b1 0.21, Utility and Trust (UT) 3.41 \u00b1 0.56, Ethical Readiness (ER) 3.62 \u00b1 0.61, Transparency and Control (TC) 3.66 \u00b1 0.64, and Institutional Support (IS) 1.86 \u00b1 0.58. The mean AETRI was 57.2 \u00b1 9.43, indicating moderate readiness. Knowledge varied by designation (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.029), with higher scores among faculty and consultants. Faculty demonstrated higher AETRI than residents (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.011). ER and TC were the main predictors of AETRI (R\n                      <jats:sup>2<\/jats:sup>\n                      = 0.52).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Anaesthesia professionals demonstrated moderate AI knowledge and trust but limited institutional readiness. Ethical readiness and transparency, rather than technical knowledge alone, are the primary determinants of clinician preparedness for AI integration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ija.ija_106_26","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:01:00Z","timestamp":1789646460000},"page":"1019-1025","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Ethical readiness and trust in artificial intelligence\u2013assisted anaesthesia: A cross-sectional survey study among anaesthesia professionals in India"],"prefix":"10.4103","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9337-296X","authenticated-orcid":false,"given":"Tiajem","family":"Jamir","sequence":"first","affiliation":[{"name":"Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8542-873X","authenticated-orcid":false,"given":"Ram","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1014-8407","authenticated-orcid":false,"given":"Prakash","family":"Gondode","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9642-194X","authenticated-orcid":false,"given":"Kunal","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9263-1680","authenticated-orcid":false,"given":"Sudhansu Sekhar","family":"Nayak","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8418-4892","authenticated-orcid":false,"given":"Prashant","family":"Sirohiya","sequence":"additional","affiliation":[{"name":"Department of Onco-Anaesthesia and Palliative Medicine, National Cancer Institute (Jhajjar), AIIMS, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"R1-20260917","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1001\/jama.2020.0592","article-title":"Effect of a machine learning-derived early warning system for intraoperative hypotension 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