Risk and liability in the deployment of AI systems for surgery: a SAGES white paper
6 affiliations
- 1Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA. [email protected]
- 2Department of Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 4 Silverstein Pavilion, Philadelphia, PA, 19104, USA. [email protected]
- 3Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- 4Department of Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 4 Silverstein Pavilion, Philadelphia, PA, 19104, USA
- 5Department of Surgery, University of Buffalo, Buffalo, NY, USA
- 6Faculty of Law, Maastricht University, Maastricht, Netherlands
Abstract
BACKGROUND: Artificial 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. METHODS: This 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. RESULTS: Risks 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. CONCLUSION: The 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.
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BibTeX
@article{hashimoto2026risk,
title = {{Risk and liability in the deployment of AI systems for surgery: a SAGES white paper}},
author = {Daniel A. Hashimoto and Jayson S. Marwaha and Sharon A. Lee and Steven D. Schwaitzberg and Mindy Duffourc},
journal = {Surgical Endoscopy},
year = {2026},
month = jun,
doi = {10.1007/s00464-026-12881-8},
url = {https://doi.org/10.1007/s00464-026-12881-8},
note = {CC BY},
}
RIS
TY - JOUR
TI - Risk and liability in the deployment of AI systems for surgery: a SAGES white paper
AU - Daniel A. Hashimoto
AU - Jayson S. Marwaha
AU - Sharon A. Lee
AU - Steven D. Schwaitzberg
AU - Mindy Duffourc
PY - 2026
DA - 2026/06/08
JO - Surgical Endoscopy
DO - 10.1007/s00464-026-12881-8
UR - https://doi.org/10.1007/s00464-026-12881-8
AB - BACKGROUND: Artificial 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. METHODS: This 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. RESULTS: Risks 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. CONCLUSION: The 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.
SN - 0930-2794
SN - 1432-2218
SN - 1866-6817
ER -