Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified Delphi Consensus
Clinical Snapshot
PICO Framework
| P — Population | Surgical practitioners and healthcare institutions implementing AI-enabled clinical decision support systems |
| I — Intervention | Consensus-driven governance framework for AI implementation in surgery |
| C — Comparator | Current ad-hoc or inconsistent AI implementation practices |
| O — Outcomes | 19-item governance framework across 4 themes: technical prerequisites, clinical implementation, ethics/safety, and bias/equity considerations |
Bottom Line
This systematic review and modified Delphi consensus study addresses a critical gap in surgical practice by developing a comprehensive 19-item governance framework for implementing AI-enabled clinical decision support systems. The framework covers four essential domains: technical prerequisites, clinical implementation considerations, ethics and safety, and bias and equity issues. While the methodology appears robust, combining systematic literature review with expert consensus, some methodological details are unclear from the abstract. The framework provides practical guidance for surgical departments and healthcare institutions seeking to implement AI technologies safely and ethically. For Australian practice, this framework could inform institutional policies and support compliance with TGA regulations for software as medical devices. The consensus-driven approach enhances credibility, though the composition and geographic representation of the expert panel requires clarification. This work represents an important step toward standardising AI implementation in surgery, potentially reducing variation in care quality and mitigating algorithmic bias risks.
Key Findings
Effect Size: Not applicable - consensus development study
Primary Outcome: 19-item governance framework across 4 themes for AI implementation in surgery
Nnt Or Sensitivity: Framework addresses 4 core domains: technical prerequisites, clinical implementation, ethics/safety, bias/equity
Clinical Application
High feasibility as framework provides structured approach to AI governance without requiring specific technology Highly relevant for Australian surgical practice given TGA regulation of software as medical devices and RACGP digital health guidelines. Framework could inform institutional AI policies and support compliance with emerging regulatory requirements. Surgical departments, healthcare institutions, and regulatory bodies implementing AI-enabled clinical decision support systems
Abstract
BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSS) demonstrate performance comparable or superior to human experts in certain tasks. However, their integration into surgical practice faces a significant implementation gap, alongside ethical, privacy, and legal concerns. Clear governance frameworks are needed to guide their responsible adoption in surgery, to prevent inconsistent application, care quality variation, and exacerbation of algorithmic bias. Here, we establish a systematic, evidence-based, and consensus-driven framework to guide the ethical, effective, and sustainable adoption of AI-enabled CDSS in surgery. STUDY DESIGN: A systematic literature review was conducted of PubMed, Cochrane Library, Medline, and Embase databases until 2024 to identify key governance themes. The themes informed the generation of candidate items, which were then refined through a multiround expert panel consensus process using a modified Delphi approach to produce the final framework. RESULTS: Thematic analysis of 80 full-text articles meeting inclusion criteria identified 4 overarching themes for AI governance: (1) technical prerequisites and model design; (2) clinical implementation and human factors; (3) ethics, safety, and trustworthiness; and (4) bias, fairness, and equity. Panel consensus evaluation resulted in the development of a 19-item framework. CONCLUSIONS: The consensus-driven framework presented here provides foundational guidance essential for navigating the complexities of implementing AI-enabled CDSS safely and ethically in surgery. Addressing the considerations outlined across these 4 core themes can facilitate the responsible adoption of AI, accelerating the transition toward an advanced, data-driven surgical practice while mitigating potential risks.
References
- 1.Hassan, A. M., Coert, J. H., Clemens, M. W., Hassanein, A. H., Waljee, J. F., Nelson, J. A., Mehrara, B. J., & Selber, J. C. (2026). Governance framework for safe and ethical implementation of artificial intelligence in surgery: A modified Delphi consensus. Journal of the American College of Surgeons. https://doi.org/10.1097/XCS.0000000000001834
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