About this episode
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5m 50s
Picture three machines in your next operation. One watches the laparoscopic feed and flags the zone that is not safe to divide. One reads the chart and has the op note and a plain-language patient summary done before you close. One works quietly in the background and has the ICU bed request queued before anyone asks. Notice that none of them hands you a percentage. In the series finale, Drs. Mason Pearce, Tyler Loftus, and Dan Hashimoto trace surgical AI from the NSQIP risk calculator to computer vision, large language models, and agentic systems: which of these is already in operating rooms, which is arriving fast, which is still mostly promise, and how a surgeon without a machine learning degree stays in control of all three.
Hosts:
- Mason Pearce, MD, General Surgery Resident and Clinical Informatics Fellow, University of South Florida; Behind the Knife Education Fellow
- Tyler J. Loftus, MD, PhD, Acute Care Surgeon and Associate Professor of Surgery; Program Director, General Surgery Residency, University of Florida; board certified in Clinical Informatics; Past Chair, ACS Health IT Committee
- Daniel A. Hashimoto, MD, MSTR, Associate Professor of Surgery and Director of Surgical Informatics, Duke University Health System; editor of Artificial Intelligence in Surgery
Learning objectives:
- Distinguish predictive, generative, and agentic AI (models that score, read and write, or act) and give a surgical example of each.
- Trace surgical risk prediction from the regression-based ACS NSQIP calculator to machine learning models (XGBoost, POTTER, MySurgeryRisk), and explain why external, multicenter validation matters.
- Describe how computer vision recognizes operative steps and safe versus unsafe dissection zones, and why standardized annotation and real-world video are needed for those models to hold up in practice.
- Identify the strengths and failure modes of large language models (fluent but wrong output, automation bias) in documentation, perioperative triage, and value-informed proxy decision support.
- Explain what makes an AI system agentic, how errors can compound across steps, and where human oversight belongs.
- Apply a practical checklist to any surgical AI tool (data source, validation on patients like yours, drift, explainability, effect on decisions and outcomes) and seek a seat on hospital AI and clinical decision support committees.
References:
- Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217(5):833-842.e1-3. https://pubmed.ncbi.nlm.nih.gov/24055383/
- Liu Y, Ko CY, Hall BL, Cohen ME. American College of Surgeons NSQIP risk calculator accuracy using a machine learning algorithm compared with regression. J Am Coll Surg. 2023;236(5):1024-1030. https://pubmed.ncbi.nlm.nih.gov/36728295/
- Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator. Ann Surg. 2018;268(4):574-583. https://pubmed.ncbi.nlm.nih.gov/30124479/
- Bihorac A, Ozrazgat-Baslanti T, Ebadi A, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652-662. https://pubmed.ncbi.nlm.nih.gov/29489489/
- Ren Y, Adiyeke E, Guan Z, et al. MySurgeryRisk model predictions of postoperative complications and mortality. JAMA Surg. 2026;161(6):619-627. MySurgeryRisk Model Predictions of Postoperative Complications and Mortality - PubMed
- Hashimoto DA, Rosman G, Witkowski ER, et al. Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg. 2019;270(3):414-421. https://pubmed.ncbi.nlm.nih.gov/31274652/
- Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann Surg. 2022;276(2):363-369. https://pubmed.ncbi.nlm.nih.gov/33196488/
- Laplante S, Namazi B, Kiani P, et al. Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy. Surg Endosc. 2023;37(3):2260-2268. https://pubmed.ncbi.nlm.nih.gov/35918549/
- Mascagni P, Vardazaryan A, Alapatt D, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg. 2022;275(5):955-961. https://pubmed.ncbi.nlm.nih.gov/33201104/
- Zhang KC, Wu D, Lu CR, et al. Real-time multicenter semantic segmentation for laparoscopic gastrectomy: development, external validation and operating room deployment of an AI navigation system. Mil Med Res. 2026;13(1):100040. https://pubmed.ncbi.nlm.nih.gov/42559101/
- Meireles OR, Rosman G, Altieri MS, et al. SAGES consensus recommendations on an annotation framework for surgical video. Surg Endosc. 2021;35(9):4918-4929. https://pubmed.ncbi.nlm.nih.gov/34231065/
- Ban Y, Rosman G, Eckhoff JA, et al. SUPR-GAN: SUrgical PRediction GAN for event anticipation in laparoscopic and robotic surgery. IEEE Robot Autom Lett. 2022;7(2). Not indexed in PubMed. https://doi.org/10.1109/LRA.2022.3156856
- Hashimoto DA, Sambasastry SK, Singh V, et al. A foundation for evaluating the surgical artificial intelligence literature. Eur J Surg Oncol. 2024;50(12):108014. https://pubmed.ncbi.nlm.nih.gov/38360498/
- Rotenstein LS, Holmgren AJ, Thombley R, et al. Changes in clinician time expenditure and visit quantity with adoption of artificial intelligence-powered scribes: a multisite study. JAMA. 2026;335(16):1408-1417. https://pubmed.ncbi.nlm.nih.gov/41920565/
- Olson KD, Meeker D, Troup M, et al. Use of ambient AI scribes to reduce administrative burden and professional burnout. JAMA Netw Open. 2025;8(10):e2534976. https://pubmed.ncbi.nlm.nih.gov/41037268/
- Wang J, Keyes T, Liang AS, et al. An electronic health record-integrated, large language model-powered tool to triage surgical patients. JAMA Netw Open. 2026;9(8):e2629579. https://pubmed.ncbi.nlm.nih.gov/42623072/
- Nolan V, Brown M, Balch J, et al. Language models for value-informed proxy decision support. NEJM AI. 2026. Not found in PubMed. https://doi.org/10.1056/AIcs2500562
- Zhai G, Bar M, Cowan AJ, et al. AI for evidence-based treatment recommendation in oncology: a blinded evaluation of large language models and agentic workflows. Front Artif Intell. 2025;8:1683322. https://pubmed.ncbi.nlm.nih.gov/41446897/
