---
title: "Determinants, Strategies, and Outcomes of AI Implementation in Surgical Care: Systematic Review Pr"
id: "bmj-open-11-implementation-determinants-and-outcomes-of-artificial-intelligence-in-surgical"
canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-11-implementation-determinants-and-outcomes-of-artificial-intelligence-in-surgical"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "BMJ Open"
source_url: "http://bmjopen.bmj.com/cgi/content/short/16/10/e120245?rss=1"
published_at: "2026-10-07T11:48:05.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Determinants, Strategies, and Outcomes of AI Implementation in Surgical Care: Systematic Review Pr
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-11-implementation-determinants-and-outcomes-of-artificial-intelligence-in-surgical
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** BMJ Open
- **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/10/e120245?rss=1)
- **Published At:** 2026-10-07T11:48:05.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This protocol outlines a systematic review to synthesise global evidence on real-world **artificial intelligence (AI)** implementation in surgical healthcare, focusing on implementation strategies, barriers, facilitators, adoption outcomes and impacts on surgical care. - AI applications of interest include machine learning predictive analytics, computer vision, robotic assistance and clinical decision support systems across surgical specialties. - The review follows **PRISMA-P** 2015 guidance and is registered with **PROSPERO** (CRD420261305972). - Electronic searches will cover PubMed/Medline, CINAHL (EBSCO), Web of Science, Scopus, Global Index Medicus, medRxiv, IEEE Xplore and ACM Digital Library for studies from January 2000 to December 2025. - Eligible study designs include randomised controlled trials, observational studies, implementation studies, qualitative and mixed-methods research, and programme evaluations. - The primary outcome is identification and synthesis of **barriers and facilitators** to AI implementation, using framework synthesis (Carroll et al.). No meta-analysis will be performed for this primary outcome. - Secondary outcomes include implementation outcomes (adoption, feasibility, acceptability, fidelity, penetration, sustainability), implementation strategies, effects on surgical quality metrics, workflow efficiency, and cost and resource utilisation; secondary outcomes will be characterised using the **RE-AIM** framework. - Where at least three studies report comparable quantitative outcomes with effect estimates, random-effects meta-analysis will be used for those secondary quantitative outcomes. - Risk of bias assessment will use the Mixed Methods Appraisal Tool; certainty of evidence will use **GRADE** for quantitative and **GRADE-CERQual** for qualitative findings. - Planned subgroup analyses include geographic setting, surgical specialty and AI modality, conditional on having a minimum of three studies per subgroup. - Ethical approval is not required; results will be disseminated via peer-reviewed publication and conference presentations.
## Clinical Analysis & Structured Key Points
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.
## Related Clinical Research

- [Patient experience on Saudi national digital health platform Sehhaty: analysis of 97,034 app revie](https://medichelpline.com/clinical-feed/plos-one-22-patient-experience-in-national-digital-health-platforms-evidence-from-patient.md)
- [Tobacco use among Lebanese university students: prevalence, drivers, and campus policy implications](https://medichelpline.com/clinical-feed/plos-one-4-prevalence-and-determinants-of-tobacco-use-among-university-students-in-lebanon.md)
- [How to define outcomes completely in clinical trials: six-element guidance](https://medichelpline.com/clinical-feed/plos-medicine-1-guidance-for-defining-outcomes-in-clinical-trials.md)
- [New Medicare Policies Limit Important Pain Relief Options for Patients](https://medichelpline.com/clinical-feed/stat-news-1-opinion-new-medicare-policies-will-keep-important-pain-relief-from-some-patients.md)
- [Why U.S. opioid overdose deaths fell sharply — and what we still don’t know](https://medichelpline.com/clinical-feed/stat-news-2-opinion-overdose-deaths-have-plunged-we-need-to-know-why.md)

## Navigation
- [← Back to General Feed](https://medichelpline.com/clinical-feed/general.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.