ReviewSurgical endoscopy2026
Risk and liability in the deployment of AI systems for surgery: a SAGES white paper.
Review in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Artificial Intelligence and the Ethical Foundations of Cardiothoracic Surgery: Evidence, Accountability, and the Limits of Delegated Judgment.Medical sciences (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly utilized in surgical care for decision support, operative planning, intraoperative guidance, and autonomous functions. While these systems can enhance efficiency and clinical performance, they also introduce risks related to technology, human factors, legal issues, and ethics. Current regulatory and legal frameworks are not fully equipped to address the challenges of AI-assisted surgery.
methodsThis SAGES white paper synthesizes current regulatory, legal, ethical, and clinical considerations relevant to the deployment of AI systems in surgery. We review market safety regulation, data privacy law, informed consent, malpractice and liability principles, and propose a conceptual framework for understanding risk in surgical AI implementation.
resultsRisks associated with surgical AI can be understood through a tripartite framework: risks inherent to the AI system, risks introduced by the clinician-user, and risks arising from institutional deployment. These risks may manifest clinically as diagnostic error, treatment error, compromised informed consent, erosion of patient trust, threats to therapeutic autonomy, and privacy violations. Although surgeons remain the ultimate clinical decision-makers, liability may also extend to developers for defective design or failure to warn, and to institutions for negligent implementation or oversight.
conclusionThe safe integration of AI into surgery requires more than technical performance alone. Robust governance, ongoing performance surveillance, incident response pathways, clinician credentialing, and specialty-society engagement are needed to reduce harm and clarify accountability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.