Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.

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Bibliographic Details
Title: Artificial intelligence and machine learning approaches for patient safety in complex surgery: a review.
Authors: Ahmed MM; Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia. momustafahmed@simad.edu.so., Othman ZK; Department of Pharmacy, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq., Adebayo UO; Department of Medical Laboratory Science, Neuropsychiatric Hospital, Aro, Abeokuta, Nigeria., Kasimieh O; University of the East Ramon Magsaysay Memorial Medical Center, Quezon City, Philippines., Okesanya OJ; Department of Medical Laboratory Science, Neuropsychiatric Hospital, Aro, Abeokuta, Nigeria.; Department of Public Health and Maritime Transport, University of Thessaly, Volos, Greece., Musa SS; School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Branda F; Unit of Medical Statistics and Molecular Epidemiology, Campus Bio- Medico University of Rome, Rome, Italy., Cañezo VC Jr; Office of the University President, Biliran Province State University, Naval, Leyte, Philippines., Cue EG; Office of the University President, Mountain Province State University, Bontoc, Mountain Province, Philippines., Prisno Iii DEL; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.; Office for Research, Extension and Innovations, Bukidnon State University, Malaybalay City, Bukidnon, Philippines.; Research Office, Palompon Institute of Technology, Palompon, Leyte, Philippines.
Source: Patient safety in surgery [Patient Saf Surg] 2025 Nov 25; Vol. 19 (1), pp. 33. Date of Electronic Publication: 2025 Nov 25.
Publication Type: Journal Article; Review
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101319176 Publication Model: Electronic Cited Medium: Print ISSN: 1754-9493 (Print) Linking ISSN: 17549493 NLM ISO Abbreviation: Patient Saf Surg Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1754-9493
DOI:10.1186/s13037-025-00458-8