Implementation determinants and outcomes of artificial intelligence in surgical healthcare: a systematic review protocol
8 affiliations
- 1RESTORE Hub, Arkansas State University System, Jonesboro, Arkansas, USA
- 2Department of Research and Innovations, Reach Apac, Apac, Uganda
- 3Faculty of Health Sciences, Habib Medical School, Islamic University in Uganda, Mbale, Uganda
- 4Dean B. Ellis Library, Arkansas State University, Jonesboro, Arkansas, USA
- 5Department of Surgery, Mbarara University of Science and Technology, Mbarara, Uganda
- 6Department of Surgery, Yumbe Regional Referral Hospital, Yumbe, Uganda
- 7School of Public Health, Makerere University CHS, Kampala, Uganda
- 8Department of Surgery, University of Toledo, Toledo, Ohio, USA
Abstract
Introduction: Artificial intelligence (AI) is increasingly being integrated into surgical healthcare to enhance perioperative risk assessment, improve diagnostic accuracy, refine clinical workflows, support intraoperative decision-making and reduce postoperative complications. AI applications including machine learning-based predictive analytics, computer vision, robotic assistance and clinical decision support systems have shown promising clinical performance across surgical specialties. However, knowledge on real-world implementation, barriers, facilitators and system-level outcomes in surgical healthcare remains fragmented with limited synthesis of implementation determinants. This systematic review aims to identify, categorise and synthesise global evidence on implementation strategies, barriers, facilitators, adoption outcomes and reported impacts of AI applications in surgical healthcare settings.
Methods and analysis: This protocol is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 guidelines and has been registered with PROSPERO (CRD420261305972). A systematic search will be conducted across PubMed/Medline, CINAHL (EBSCO), Web of Science, Scopus, Global Index Medicus, medRxiv, IEEE Xplore and ACM Digital Library for studies published between January 2000 and December 2025. Study designs include interventional (randomised controlled trials), observational, implementation, qualitative and mixed-methods studies and programme evaluations. The primary outcome is barriers and facilitators to AI implementation, and synthesised using framework synthesis (Carroll et al ). Secondary outcomes include implementation outcomes (eg, adoption, feasibility, acceptability, fidelity, penetration, and sustainability), implementation strategies employed, reported effects on surgical quality metrics, workflow efficiency, and cost and resource utilisation. Secondary implementation outcomes will be characterised using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework. Where three or more studies report comparable quantitative outcome data with effect estimates, random-effects meta-analysis will be conducted; no meta-analysis will be performed for the primary outcome. Risk of bias will be assessed using the Mixed Methods Appraisal Tool. Certainty of evidence will be evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE) for quantitative findings and GRADE-CERQual for qualitative evidence. Subgroup analyses by geographic setting, surgical specialty and AI modality are planned, conditional on data availability (minimum three studies per subgroup).
Ethics and dissemination: Ethical approval is not needed, and the results will be disseminated via peer-reviewed publication and presentation at conferences relevant to this field.
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BibTeX
@article{otile2026implementation,
title = {{Implementation determinants and outcomes of artificial intelligence in surgical healthcare: a systematic review protocol}},
author = {Jacob Otile and Robert Robinette and Kristin Johnson and RamaKoti Reddy Peram and Michael Ndyomugabe and Emanuel Mujuni and Ibrahim Mohamed Abdulla and Aisha Yussuf Khalif and Wandera Uthmaan Muluga and Charles Batholomae and Duangnapa Cuddy},
journal = {BMJ Open},
year = {2026},
month = oct,
doi = {10.1136/bmjopen-2026-120245},
url = {https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2026-120245},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Implementation determinants and outcomes of artificial intelligence in surgical healthcare: a systematic review protocol
AU - Jacob Otile
AU - Robert Robinette
AU - Kristin Johnson
AU - RamaKoti Reddy Peram
AU - Michael Ndyomugabe
AU - Emanuel Mujuni
AU - Ibrahim Mohamed Abdulla
AU - Aisha Yussuf Khalif
AU - Wandera Uthmaan Muluga
AU - Charles Batholomae
AU - Duangnapa Cuddy
PY - 2026
DA - 2026/10/07
JO - BMJ Open
DO - 10.1136/bmjopen-2026-120245
UR - https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2026-120245
AB - Introduction: Artificial intelligence (AI) is increasingly being integrated into surgical healthcare to enhance perioperative risk assessment, improve diagnostic accuracy, refine clinical workflows, support intraoperative decision-making and reduce postoperative complications. AI applications including machine learning-based predictive analytics, computer vision, robotic assistance and clinical decision support systems have shown promising clinical performance across surgical specialties. However, knowledge on real-world implementation, barriers, facilitators and system-level outcomes in surgical healthcare remains fragmented with limited synthesis of implementation determinants. This systematic review aims to identify, categorise and synthesise global evidence on implementation strategies, barriers, facilitators, adoption outcomes and reported impacts of AI applications in surgical healthcare settings. Methods and analysis: This protocol is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 guidelines and has been registered with PROSPERO (CRD420261305972). A systematic search will be conducted across PubMed/Medline, CINAHL (EBSCO), Web of Science, Scopus, Global Index Medicus, medRxiv, IEEE Xplore and ACM Digital Library for studies published between January 2000 and December 2025. Study designs include interventional (randomised controlled trials), observational, implementation, qualitative and mixed-methods studies and programme evaluations. The primary outcome is barriers and facilitators to AI implementation, and synthesised using framework synthesis (Carroll et al ). Secondary outcomes include implementation outcomes (eg, adoption, feasibility, acceptability, fidelity, penetration, and sustainability), implementation strategies employed, reported effects on surgical quality metrics, workflow efficiency, and cost and resource utilisation. Secondary implementation outcomes will be characterised using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework. Where three or more studies report comparable quantitative outcome data with effect estimates, random-effects meta-analysis will be conducted; no meta-analysis will be performed for the primary outcome. Risk of bias will be assessed using the Mixed Methods Appraisal Tool. Certainty of evidence will be evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE) for quantitative findings and GRADE-CERQual for qualitative evidence. Subgroup analyses by geographic setting, surgical specialty and AI modality are planned, conditional on data availability (minimum three studies per subgroup). Ethics and dissemination: Ethical approval is not needed, and the results will be disseminated via peer-reviewed publication and presentation at conferences relevant to this field.
SN - 2044-6055
SN - 2044-6055
ER -