Behind The Knife: The Surgery Podcast

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/

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A case goes wrong. There is a complication, maybe a lawsuit, and suddenly the question is not just what happened in the room. It is what got recorded, who owns it, what a plaintiff's attorney can pull into discovery, and whether that same pile of data is a target for people who want to steal it. Join Drs. Scott Steele, Mason Pearce, and Teodor Grantcharov, the surgeon who built the OR Black Box, as they walk through what an instrumented operating room actually captures, why peer review privilege protects the review and not the facts, how AI outputs create a new evidentiary layer, and why cybersecurity is a patient safety issue. The surprising part: governed recording more often protects surgeons than exposes them.
Hosts:

Scott Steele, MD, MBA, Colon and Rectal Surgeon; President, Cleveland Clinic Main Campus 

Mason Pearce, MD, General Surgery Resident and Clinical Informatics Fellow, University of South Florida; Behind the Knife Education Fellow 

Teodor Grantcharov, MD, PhD, Professor of Surgery, Stanford University; Associate Chief Quality Officer for Innovation and Safety, Stanford Health Care; Founder, Aimbient (formerly Surgical Safety Technologies), the company behind the OR Black Box
Linkedin: https://www.linkedin.com/in/teodorgrantcharov/?isSelfProfile=true 

Aimbient’s: https://www.linkedin.com/company/aimbient/

Disclosure: Dr. Grantcharov is the founder of Aimbient (formerly Surgical Safety Technologies). Platform figures discussed on air (hours of data, number of sites and rooms, data volume per OR-hour, retention period, security record, publication count) are company-reported.

Learning objectives:

Describe the data an instrumented operating room generates (field and room video, audio, device telemetry, EHR timestamps) and how ownership is layered across the institution, the patient's right of access, and the vendor.

Distinguish material that is routinely discoverable (the medical record, operative and anesthesia notes, device logs) from contested material (intraoperative video and quality review analytics).

Explain why peer review protections cover the review process rather than the underlying facts, vary by state, and depend on how the institution structures its quality program.

Recognize how AI alerts and risk scores add a new evidentiary layer to clinical decisions.

Explain why cybersecurity is a patient safety issue, using the 2024 Change Healthcare ransomware attack as an example, and name the security basics (multifactor authentication, least-privilege access, security risk analysis) surgeons should ask about.

Apply three practical steps: know who the custodian of your operative data is, insist that recording programs run as governed quality and peer review programs, and ask whether a security risk analysis was done before any new vendor goes live.

References:

Goldenberg MG, Jung J, Grantcharov TP. Using data to enhance performance and improve quality and safety in surgery. JAMA Surg. 2017;152(10):972-973. https://pubmed.ncbi.nlm.nih.gov/28813552/

Jung JJ, Jüni P, Lebovic G, Grantcharov T. First-year analysis of the operating room black box study. Ann Surg. 2020;271(1):122-127. https://pubmed.ncbi.nlm.nih.gov/29916872/

Prigoff JG, Sherwin M, Divino CM. Ethical recommendations for video recording in the operating room. Ann Surg. 2016;264(1):34-35. https://pubmed.ncbi.nlm.nih.gov/27123809/

Filicori F, Addison P. Intellectual property and data ownership in the age of video recording in the operating room. Surg Endosc. 2022;36(6):3772-3774. https://pubmed.ncbi.nlm.nih.gov/34468846/

van Dalen ASHM, Legemaate J, Schlack WS, Legemate DA, Schijven MP. Legal perspectives on black box recording devices in the operating environment. Br J Surg. 2019;106(11):1433-1441. https://pubmed.ncbi.nlm.nih.gov/31112294/

van de Graaf FW, Lange MM, Spakman JI, et al. Comparison of systematic video documentation with narrative operative report in colorectal cancer surgery. JAMA Surg. 2019;154(5):381-389. https://pubmed.ncbi.nlm.nih.gov/30673072/

Succar B, Vella MA, Holena D, et al. Trauma video review through the legal lens: improving care while minimizing risk. J Trauma Acute Care Surg. 2024;97(6):978-982. https://pubmed.ncbi.nlm.nih.gov/39240137/

Lavanchy JL, Vardazaryan A, Mascagni P, et al. Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos. Sci Rep. 2023;13(1):9235. https://pubmed.ncbi.nlm.nih.gov/37286660/

Neprash HT, McGlave CC, Cross DA, et al. Trends in ransomware attacks on US hospitals, clinics, and other health care delivery organizations, 2016-2021. JAMA Health Forum. 2022;3(12):e224873. https://pubmed.ncbi.nlm.nih.gov/36580326/

Kels CG. Odd man out? The medical peer review privilege in federal litigation. The Federal Lawyer. December 2013. Covers the Health Care Quality Improvement Act (immunity, no discovery privilege) and the Patient Safety and Quality Improvement Act of 2005. Not indexed in PubMed. https://www.fedbar.org/wp-content/uploads/2013/12/feature5-dec13-pdf-1.pdf

Healthcare IT News. New numbers from the Change Healthcare data breach: 193 million affected. August 6, 2025. Not indexed in PubMed. New numbers from the Change Healthcare data breach: 193 million affected | Healthcare IT News

Kapko M. Change Healthcare, compromised by stolen credentials, did not have MFA turned on. Healthcare Dive. April 30, 2024. Not indexed in PubMed. Change Healthcare, compromised by stolen credentials, did not have MFA turned on | Healthcare Dive

Alder S. Average cost of a healthcare data breach falls to $7.42 million (IBM Cost of a Data Breach Report 2025). The HIPAA Journal. July 30, 2025. Not indexed in PubMed. Average Cost of a Healthcare Data Breach Falls to $7.42 Million

The HIPAA Journal. Healthcare data breach statistics. Updated October 2026. Not indexed in PubMed. Healthcare Data Breach Statistics

American Medical Association. Medical cybersecurity: a patient safety issue. Not indexed in PubMed. https://www.ama-assn.org/practice-management/sustainability/medical-cybersecurity-patient-safety-is…

Surgical Safety Technologies rebrands as Aimbient, broadens ambient AI platform. Healthcare Innovation. July 8, 2026. Not indexed in PubMed. Surgical Safety Technologies Rebrands as Aimbient, Broadens Ambient AI Platform

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You click an order set and ten seconds later the labs, imaging and meds are in. A drug interaction alert fires, and you override it without reading. A sepsis score in the corner of the chart flags your post-op patient. Who built those defaults, who set that alert threshold, and was that score ever validated on surgical patients? Join Drs. Kevin Kniery, Mason Pearce, and Genevieve Melton-Meaux, a practicing colorectal surgeon and the American College of Surgeons' first Chief Health Informatics Officer, as they kick off the Clinical Informatics for Surgeons series. They explain what clinical informatics actually is (hint: not the help desk), how it is already running your operative day, and the few habits that let any surgeon shape the tools everyone else is stuck with.
Hosts:

Genevieve Melton-Meaux, MD, PhD, Colon and Rectal Surgeon; Senior Associate Dean for Health Informatics and Data Science, University of Minnesota; Chief Health Informatics Officer, American College of Surgeons; Chair, AMIA Board of Directors 

Kevin Kniery, MD, FACS, Vascular Surgeon, Brooke Army Medical Center

Mason Pearce, MD, General Surgery Resident and Clinical Informatics Fellow, University of South Florida; Behind the Knife Education Fellow 

Learning objectives:

Define clinical informatics, distinguish it from information technology, and describe how physicians train and certify in the subspecialty (ACGME-accredited fellowship, ABPM/ABPath board exam).

Identify four everyday systems that shape surgical care (order set defaults, medication alerts, embedded risk scores, OR scheduling logic) and recognize them as clinical design decisions.

Explain alert fatigue as a patient safety problem and why independent external validation of predictive models matters, using the Epic Sepsis Model as an example.

Describe the three core areas of the surgeon informatician: clinical decision-making and care process improvement; health information systems, data and analytics; and leadership and change management.

Apply practical habits for informatics literacy: question a risk score's data and validation, report faulty alerts, and join the committees that maintain order sets and decision support.

References:

Felisberto M, Lima GDS, Celuppi IC, et al. Override rate of drug-drug interaction alerts in clinical decision support systems: a brief systematic review and meta-analysis. Health Informatics J. 2024;30(2):14604582241263242. https://pubmed.ncbi.nlm.nih.gov/38899788/

Ashton M. Getting rid of stupid stuff. N Engl J Med. 2018;379(19):1789-1791. https://pubmed.ncbi.nlm.nih.gov/30403948/

Rajamani G, Diethelm M, Gunderson MA, et al. Crowdsourcing electronic health record improvements at scale across an integrated health care delivery system. Appl Clin Inform. 2023;14(2):356-364. https://pubmed.ncbi.nlm.nih.gov/37164355/

Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065-1070. https://pubmed.ncbi.nlm.nih.gov/34152373/

Osheroff J, Teich J, Levick D, Saldana L, Velasco F, Sittig D, Rogers K, Jenders R. Improving Outcomes with Clinical Decision Support: An Implementer's Guide. 2nd ed. HIMSS Publishing; 2012. ISBN 9780984457731. Book; not indexed in PubMed. Source of the CDS Five Rights framework. https://www.routledge.com/Improving-Outcomes-with-Clinical-Decision-Support-An-Implementers-Guide-S…

Gardner RM, Overhage JM, Steen EB, et al. Core content for the subspecialty of clinical informatics. J Am Med Inform Assoc. 2009;16(2):153-157. https://pubmed.ncbi.nlm.nih.gov/19074296/

Zhao J, Forsythe R, Langerman A, Melton GB, Schneider DF, Jackson GP. The value of the surgeon informatician. J Surg Res. 2020;252:264-271. https://pubmed.ncbi.nlm.nih.gov/32402396/

Anderson TN, Jenkins PD, Doberne JW, Mohan V, Thanawala RM. Defining the clinical informatics knowledge gap in general surgery: evaluation of resident experience and perspectives. J Surg Educ. 2025;82(11):103682. https://pubmed.ncbi.nlm.nih.gov/40911943/

Lyu HG, Doherty GM, Landman AB. Surgical informatics: defining the role of informatics in the current surgical training paradigm. J Surg Educ. 2020;77(1):9-12. https://pubmed.ncbi.nlm.nih.gov/31477548/

AMIA. AMIA's role in the clinical informatics subspecialty and history (ABMS approval 2011; first exam October 2013). AMIA's Role in CIS and History

Turner PL. Exploring artificial intelligence at the ACS. Bulletin of the American College of Surgeons. October 2024. Exploring Artificial Intelligence at the ACS | ACS

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How do we care for ourselves while caring for others?
Surgery is a demanding profession that requires resilience, sacrifice, and the ability to navigate high-stakes decisions. However, depression, anxiety, burnout, and the emotional consequences of complications are realities that many surgeons face but often struggle to discuss openly.
In this episode of the Behind the Knife/Association of Surgeons of Great Britain and Ireland (ASGBI) collaborative series, our guests share their personal experiences navigating mental health challenges throughout their careers, confronting stigma, finding support, and learning that being a successful surgeon does not require being perfect.
Hosts

Dr. Agnes Premkumar — Behind the Knife Surgical Education Fellow, General Surgery resident (US)

Dr. Gita Lingham — President, Moynihan Academy, trainee association within the Association of Surgeons of Great Britain and Ireland (ASGBI) (UK)

Dr. Elizabeth McLennan — Vice President, Moynihan Academy, trainee association within the Association of Surgeons of Great Britain and Ireland (ASGBI) (UK)

Guests

Dr. Luke Elms — Board-certified general surgeon specializing in abdominal wall reconstruction and hernia surgery; host of The Hernia Gods podcast.

Dr. Elizabeth “Liz” Miller — Former neurosurgical trainee and current occupational health physician, mental health advocate, and author of Mood Mapping.

Resources Mentioned
Mood Mapping — Dr. Elizabeth “Liz” Miller
A practical guide to understanding and managing emotional well-being through mood awareness, self-reflection, and strategies such as exercise, music, spending time outdoors, and connecting with others.
The Hernia Gods Podcast — Dr. Luke Elms
A podcast exploring the humanity behind surgery, including imposter syndrome, complications, professional identity, self-doubt, and the shared experiences of surgeons at every career stage.
Available on Apple Podcasts, Spotify, and YouTube.

Doctors' Support Network
https://www.dsn.org.uk/. A peer-support initiative discussed by Dr. Miller, emphasizing compassion, connection, and reducing the stigma associated with mental health challenges among physicians.
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The scrub tech holds up two boxes, “SURGICEL or EVARREST?”, and you realize nobody ever taught you the difference. Patrick Georgoff, MD, John McClellan, MD, Kevin Kniery, MD, and Nick Teman, MD, break down topical hemostatic agents, one of the most used and least taught tools in the OR. You’ll get a simple two-question model for picking the right product: can your patient make their own clot, and can a patch reach the bleeding? Through trauma, vascular, and cardiac cases, the team covers when these agents earn their keep and when two minutes of pressure (no peeking!) will do the job. Bottom line: hemostatic agents speed up hemostasis, but they’re a targeted adjunct to good surgical technique, not a substitute for it!
Hosts:

Patrick Georgoff, MD, Duke

Nick Teman, MD, University of Colorado

John McCllelan, MD, UNC

Kevin Kniery, MD, Madigan Army Medical Center

Topical Hemostatic Agent PDF link: https://drive.google.com/file/d/1Qv9V_0l20CTXKQ_83newveIahmxzlpcb/view?usp=sharing
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