{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T14:59:02Z","timestamp":1790348342950,"version":"4.1.0"},"reference-count":17,"publisher":"BMJ","issue":"9","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"unspecified","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2026,9,10]]},"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Perioperative pain is a major determinant of patients\u2019 experience and may influence long-term outcomes. Artificial intelligence (AI) is applied to perioperative datasets to predict acute postoperative pain, opioid requirements, analgesic-related adverse events, chronic postsurgical pain and pain trajectories. However, the evidence is dispersed across perioperative phases, clinical outcomes, data modalities and AI methodologies, while the extent to which current models address validation, interpretability, uncertainty and clinical implementation remains unclear. This scoping review protocol aims to characterise the existing evidence on AI applications related to perioperative pain to identify methodological features that affect its clinical credibility and implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>This protocol will follow the Joanna Briggs Institute methodology for scoping reviews and will be reported in line with Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidance. Eligible studies will include human research evaluating AI methods in relation to pain-related perioperative outcomes, including observational studies, interventional studies and predictive modelling studies. AI approaches include machine learning, deep learning, natural language processing, computer vision, large language models and hybrid or ensemble methods. Searches will be conducted from PubMed\/MEDLINE, Embase, the Cochrane Library, medRxiv, arXiv and ClinicalTrials.gov, with supplementary screening of reference lists. Results will be synthesised descriptively. The formal literature searches are planned for September 2026, with completion of study selection, data charting, evidence synthesis and preparation of the final review expected by December 2026.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>We will chart ethical aspects reported in included studies, such as governance of retrospective electronic health record use, consent waivers and privacy protections, where available. Findings will be disseminated through submission to a peer-reviewed journal, presentation at scientific meetings and open sharing of search strategies on the Open Science Framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>PROSPERO registration number<\/jats:title>\n                    <jats:p>\n                      This protocol was prospectively registered on the Open Science Framework in March 2026: Cascella M\n                      <jats:italic>et al<\/jats:italic>\n                      \u2018Artificial intelligence in perioperative pain: a scoping review protocol\u2019. DOI: 10.17605\/OSF.IO\/SYZH8.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2026-124432","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T14:05:36Z","timestamp":1790345136000},"page":"e124432","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in perioperative pain: a scoping review protocol"],"prefix":"10.1136","volume":"16","author":[{"given":"Aniello","family":"Alfieri","sequence":"first","affiliation":[{"name":"Department of Women\u2019s, Children\u2019s and General and Specialised Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, Napoli, Italy"},{"name":"Department of Intensive Care and Anaesthesiology, AORN A. Cardarelli, Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5657-6959","authenticated-orcid":false,"given":"Valentina","family":"Cerrone","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry \u201cScuola Medica Salernitana\u201d, University of Salerno, Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sveva","family":"Di Franco","sequence":"additional","affiliation":[{"name":"Department of Intensive Care and Anaesthesiology, AORN A. Cardarelli, Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Cascella","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry \u201cScuola Medica Salernitana\u201d, University of Salerno, Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosario","family":"De Feo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry \u201cScuola Medica Salernitana\u201d, University of Salerno, Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0316-5930","authenticated-orcid":false,"given":"Ornella","family":"Piazza","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry \u201cScuola Medica Salernitana\u201d, University of Salerno, Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Pia","family":"Bruno","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry \u201cScuola Medica Salernitana\u201d, University of Salerno, Baronissi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7263-0229","authenticated-orcid":false,"given":"Marco","family":"Fiore","sequence":"additional","affiliation":[{"name":"Department of Women\u2019s, Children\u2019s and General and Specialised Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"2026092507051666000_16.9.e124432.1","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1111\/anae.16391","article-title":"Peri-operative pain management in adults: a multidisciplinary consensus statement from the Association of Anaesthetists and the British Pain Society","volume":"79","author":"El-Boghdadly","year":"2024","journal-title":"Anaesthesia"},{"key":"2026092507051666000_16.9.e124432.2","doi-asserted-by":"publisher","DOI":"10.2147\/JPR.S144066"},{"key":"2026092507051666000_16.9.e124432.3","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.bja.2024.06.031","article-title":"Challenges in Enhanced Recovery After Surgery (ERAS) research","volume":"133","author":"Lobo","year":"2024","journal-title":"Br J Anaesth"},{"key":"2026092507051666000_16.9.e124432.4","doi-asserted-by":"publisher","DOI":"10.1097\/j.pain.0000000000001413"},{"key":"2026092507051666000_16.9.e124432.5","doi-asserted-by":"publisher","DOI":"10.3344\/kjp.2018.31.3.155"},{"key":"2026092507051666000_16.9.e124432.6","doi-asserted-by":"crossref","first-page":"S40","DOI":"10.4103\/joacp.JOACP_51_18","article-title":"Multimodal analgesia as an essential part of enhanced recovery protocols in the ambulatory settings","volume":"35","author":"Kaye","year":"2019","journal-title":"J Anaesthesiol Clin Pharmacol"},{"key":"2026092507051666000_16.9.e124432.7","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2024.1316931","article-title":"Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine","volume":"6","author":"Paiste","year":"2024","journal-title":"Front Digit Health"},{"key":"2026092507051666000_16.9.e124432.8","doi-asserted-by":"crossref","DOI":"10.3390\/jcm14196900","article-title":"Artificial Intelligence in Anesthesia: Enhancing Precision, Safety, and Global Access Through Data-Driven Systems","volume":"14","author":"Giri","year":"2025","journal-title":"J Clin Med"},{"key":"2026092507051666000_16.9.e124432.9","doi-asserted-by":"publisher","DOI":"10.1136\/rapm-2024-105603"},{"key":"2026092507051666000_16.9.e124432.10","doi-asserted-by":"crossref","unstructured":"Cao Y , Wang Y , Liu H , et al . n.d. Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology. 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