Surgery, Anesthesiology, and Perioperative Care
Surgery combines technical skill, anatomical knowledge, and split-second decision-making under pressure. AI applications span preoperative risk assessment, intraoperative guidance, and postoperative monitoring.
After reading this chapter, you will be able to:
- Evaluate AI systems for surgical risk prediction and optimization
- Understand computer vision applications in robotic and minimally invasive surgery
- Create evaluation plans for robotic surgery platforms and surgical video AI
- Assess AI tools for surgical phase recognition and workflow analysis
- Navigate AI-assisted surgical planning and simulation
- Identify postoperative complication prediction systems
- Recognize limitations and failure modes of surgical AI
- Balance AI augmentation with surgical judgment and technical skill
Introduction
Surgery stands apart from other medical specialties in its immediacy, irreversibility, and technical demands. While radiologists can analyze images over minutes, surgeons make split-second decisions with scalpel in hand. While internists can adjust management based on patient response, surgical decisions, once made, cannot be easily undone.
This unique context shapes how AI can and cannot help surgeons. The most promising applications assist with the cognitive work surrounding surgery (risk assessment, planning, outcome prediction) rather than replacing the surgeon’s hands or judgment during the operation itself.
The sections that follow cover surgical AI applications across the perioperative spectrum, from preoperative optimization through postoperative care.
Preoperative AI Applications
Surgical Risk Prediction
The Clinical Problem:
Surgeons face a fundamental question before every operation: Will this patient tolerate this procedure? Traditional risk assessment relies on clinical judgment supplemented by scoring systems (ASA classification, NSQIP risk calculator, RCRI for cardiac risk in non-cardiac surgery). These tools have limitations:
- Incorporate limited variables (20-30 factors)
- Use linear models that miss complex interactions
- Provide population-level estimates, not personalized predictions
- Updated infrequently as new evidence emerges
Machine Learning Solutions:
Modern ML approaches improve risk prediction by:
- Analyzing larger feature sets: 100+ variables from EHR, imaging, labs, medications, vital signs, social determinants
- Capturing nonlinear relationships: Age × frailty × procedure complexity interactions
- Continuous learning: Models updated with new outcome data
- Personalized predictions: Patient-specific risk estimates rather than population averages
Evidence:
The MySurgeryRisk algorithm developed at the University of Florida was evaluated retrospectively across a large surgical dataset (Bihorac et al., 2019):
- 30-day mortality prediction: AUC 0.77-0.83 across follow-up periods (the 0.94 figure is the upper end of the complications range, not mortality)
- Major complications: AUC range 0.82-0.94 across 8 complication types
- ICU admission: AUC within the 0.82-0.94 range (individual breakdown not separately reported in abstract)
- Hospital length of stay: Better calibration across risk spectrum
These discrimination results do not establish that displaying the risk estimate improves shared decisions, complications, length of stay, or mortality.
Clinical Applications:
Preoperative Optimization:
- Identify modifiable risk factors (anemia, hyperglycemia, nutritional deficits)
- Triage patients for preoperative clinic vs. day-of-surgery admission
- Guide prehabilitation referrals
Shared Decision-Making:
- Provide personalized risk estimates during surgical consults
- Facilitate discussions about alternative treatments
- Support goals-of-care conversations for high-risk patients
Resource Allocation:
- Predict ICU vs. floor bed requirements
- Identify patients needing enhanced postoperative monitoring
- Optimize OR scheduling based on predicted case duration
Quality Improvement:
- Risk-adjust outcome comparisons between surgeons/hospitals
- Identify outliers for focused improvement efforts
- Benchmark performance against predicted outcomes
Critical Limitations:
Risk calculators should inform, not dictate, surgical decisions:
- Algorithms miss important factors: patient goals, functional trajectory, social support, frailty nuances
- High-risk patients may still benefit from surgery if alternative is certain poor outcome
- Low-risk predictions don’t guarantee good outcomes
- Models trained on one population may not generalize to different populations
Clinical Bottom Line: Use risk prediction AI to enhance shared decision-making and optimize preoperative preparation. Do not deny surgery based solely on algorithmic risk scores.
Preoperative Planning and Simulation
AI-Assisted Anatomical Segmentation:
Surgical planning for complex cases (oncologic resections, liver surgery, orthopedic reconstructions) traditionally requires manual analysis of CT/MRI to identify anatomy, plan approaches, and anticipate challenges. AI automates and enhances this process:
Applications:
Oncologic Surgery:
- Tumor segmentation and volumetry
- Relationship to critical structures (vessels, bile ducts, nerves)
- Predicted resection margins
- Assessment of resectability
Liver Surgery:
- Vascular and biliary anatomy mapping
- Liver volumetry for donation or resection planning
- Future liver remnant calculation
- Virtual hepatectomy simulation
Orthopedic Surgery:
- Joint replacement planning (alignment, component sizing)
- Osteotomy planning for deformity correction
- Fracture reduction simulation
- Bone tumor resection planning
Neurosurgery:
- Brain tumor segmentation and eloquent cortex mapping
- Surgical approach trajectory planning
- Vascular anatomy for aneurysm clipping
- Epilepsy focus localization
Evidence boundary: Segmentation research demonstrates task performance in selected datasets. It does not establish a class-wide reduction in planning time, improved counseling, or better surgical outcomes (Hashimoto et al., 2018). A segmentation metric is not evidence that a surgical plan is safe or effective.
Limitations:
- Segmentation errors can propagate to surgical plans (always verify)
- Quality depends on input imaging (motion artifacts, contrast timing)
- Doesn’t account for intraoperative findings (adhesions, variant anatomy)
- Most effective for anatomy-driven procedures with good imaging
3D Printing and Surgical Models:
AI-segmented anatomy can be converted to 3D-printed models for:
- Pre-surgical rehearsal of complex cases
- Patient education and consent
- Trainee education
- Custom surgical guides and implants
Clinical Impact: Mixed. Some studies show reduced operative time and improved outcomes for complex cases; others show no benefit beyond surgeon confidence. Cost and workflow integration remain barriers to widespread adoption.
Intraoperative AI Applications
Computer Vision in Minimally Invasive Surgery
The laparoscope and robotic camera create continuous video streams, ideal data for computer vision AI. Applications range from documentation to real-time guidance, with varying degrees of validation and clinical readiness.
Surgical Phase Recognition:
What it does: AI analyzes surgical video and identifies current phase (e.g., “dissection of gallbladder from liver bed” in laparoscopic cholecystectomy)
How it works: Deep learning models trained on annotated surgical videos learn to recognize instrument configurations, anatomical landmarks, and surgeon actions characteristic of each phase.
Performance:
- Accuracy approximately 82% for laparoscopic cholecystectomy phase recognition (Twinanda et al., 2017)
- Works across multiple procedures (bariatric, colorectal, gynecologic)
- Real-time capability (15-30 frames/second)
Potential applications:
- Context-aware instrument tracking
- Automated surgical documentation
- OR efficiency analysis
- Surgical skill assessment
- Adverse event detection
Current status: Primarily research tool. Limited clinical deployment because phase recognition alone doesn’t provide actionable guidance. Surgeons already know which phase they’re in.
Future potential: Phase recognition is foundational for more advanced applications (predictive alerts, context-aware instrument suggestions).
Anatomical Structure Recognition:
The promise: Computer vision identifies critical anatomy (bile ducts, ureters, vessels) to prevent surgical injury.
The reality: This is extraordinarily difficult and not yet clinically reliable.
Why it’s hard:
- Visual variability: Blood, smoke, retraction, lighting changes, cautery artifacts
- Anatomical variants: Textbook anatomy is the exception, not the rule
- Dynamic deformation: Tissue moves, stretches, changes appearance continuously
- Occlusion: Critical structures often partially hidden
- Context-dependence: What looks like ureter may be vessel or adhesion band
Current evidence: Research systems report structure-recognition performance in selected video datasets. Bleeding, smoke, occlusion, tissue deformation, and unfamiliar anatomy can change the input distribution. Dataset accuracy does not authorize irreversible intraoperative action.
Critical safety concern:
Surgeons cannot rely on AI to definitively identify critical structures. Visual confirmation, tactile feedback, anatomical knowledge, and methodical dissection remain essential. AI suggesting “safe to divide this structure” is not acceptable with current technology.
Uncertainty display and abstention should be evaluated explicitly rather than assuming that a warning design is safer.
AI in Robotic Surgery
Robotic surgery belongs inside surgical AI evaluation, not as a separate specialty. It is a platform category, a surgical training problem, a data-capture layer for surgical AI, and a possible future path for supervised autonomy. Evaluation must separate the robot, the surgeon, the procedure, the AI layer, and the local health-system context.
Current state: teleoperation, not autonomy
Current clinical soft-tissue robotic platforms are teleoperated minimally invasive systems. The surgeon controls instrument motion from a console. The platform may provide three-dimensional visualization, wristed instruments, tremor filtering, motion scaling, ergonomic benefits, and integrated data capture, but those features do not equal autonomous surgical judgment.
The installed base and procedure volume are now large enough that robotic surgery requires routine service-line governance rather than innovation-lab treatment. Intuitive Surgical reported approximately 3.153 million da Vinci procedures in 2025, compared with approximately 2.683 million in 2024, and reported a system installed base of more than 12,100 by year-end 2025 (Intuitive Surgical, 2026 annual report).
FDA status is platform-specific
FDA clearance or authorization applies to specific devices, indications, and intended uses. It does not validate every hospital’s use case, every surgeon’s learning curve, or every AI claim layered on top of the robotic platform.
| Platform | FDA status | Current relevance |
|---|---|---|
| da Vinci 5 | 510(k) clearance, K232610, March 2024 | Fifth-generation multiport da Vinci system. Clearance supports the platform’s intended use, not autonomous surgery (FDA K232610). |
| Versius Surgical System | De Novo authorization, DEN230078, October 2024 | Class II modular electromechanical surgical system, initially indicated in the United States for adult cholecystectomy (FDA DEN230078). |
| Hugo RAS System | 510(k) clearance, K250725, December 2025 | Class II modular electromechanical surgical system for adult minimally invasive urologic surgical procedures (FDA K250725). |
FDA status should be verified from FDA records before procurement, credentialing, patient-facing materials, or handbook updates. Vendor announcements can describe launch strategy and training ecosystems, but FDA records define the cleared indication.
Commercial landscape and evidence status
Commercial activity now falls into distinct evidence categories. FDA-authorized teleoperated platforms, including da Vinci, Versius, and Hugo, should be evaluated by device indication and procedure-specific outcomes. Surgical video and operating-room analytics companies, including OR Black Box, Touch Surgery, Theator, and Caresyntax, should be evaluated as data-capture, documentation, video-review, or quality-measurement systems; peer-reviewed evidence supports feasibility and implementation analysis, but not automatic outcome improvement (Thornton et al., 2026; Aklilu et al., 2024). Autonomy-first startups, including Aleph Surgical, signal where the market is looking next, but Aleph’s March 2026 research preview is not peer-reviewed clinical evidence and no FDA authorization or clinical outcomes publication was located as of June 18, 2026. Treat these companies as systems to track, not as evidence of clinical readiness.
Surgical World Models
Surgical world models predict how an operative scene changes after an instrument action. They can support simulation research and synthetic training data, but visually plausible video does not establish tissue fidelity or operative intent. In a 2025 pilot preprint, four surgeons found that higher-level plausibility deteriorated over an eight-second prediction horizon even when generated frames remained visually convincing (Chen et al., 2025, preprint). Procurement and autonomy claims should therefore be evaluated against action-conditioned tissue response, uncertainty, horizon-specific error, and failure recovery. The broader embodied-systems evidence is summarized in Emerging AI Technologies.
Recent clinical evidence
Robotic surgery does not have one evidence grade. Each procedure has its own learning curve, comparator, outcomes, and cost structure.
| Evidence question | Recent finding | Evidence quality |
|---|---|---|
| Middle and low rectal cancer | The REAL randomized trial included 1171 patients with median 43-month follow-up and found lower 3-year locoregional recurrence with robotic surgery than laparoscopy (1.6% vs. 4.0%) and higher disease-free survival (87.2% vs. 83.4%), with similar overall survival (Feng et al., 2025). | Moderate to high: randomized trial, but high-volume expert centers in China limit generalization. |
| Acute-care cholecystectomy | A propensity-matched JAMA Surgery cohort found similar bile duct injury rates for robotic-assisted and laparoscopic cholecystectomy, but higher major postoperative complications with robotic-assisted surgery (8.37% vs. 5.50%), more drain use, and longer length of stay (Woldehana et al., 2025). A newer JAMA Surgery analysis examined comparative safety in contemporary practice, underscoring that cholecystectomy safety should be monitored with current local outcomes rather than platform assumptions (Mullens et al., 2026). | Low to moderate: large observational cohorts, residual confounding remains possible. |
| Adoption drivers | A 2026 JAMA Network Open cohort of 20,313 surgeons found receipt of direct industry payment was associated with increased proportional use of robotic-assisted surgery, with a dose-response pattern (San Loh et al., 2026). | Low to moderate: observational policy evidence, useful for governance rather than causal proof of patient benefit. |
| Credentialing and privileging | A 2026 JAMA Surgery Viewpoint proposed competency-based, vendor-neutral privileging for robotic surgery rather than platform-specific exposure or case volume alone (Ashar & Selber, 2026). | Expert framework: useful for governance, not patient-outcome evidence. |
| Intraoperative AI implementation | A multisite qualitative study of AI-based Operating Room Black Box implementation found gaps between expectations and delivery: additional AI training needs, difficult data access, limited postoperative complication prediction, and limited academic deliverables (Thornton et al., 2026). | Low: qualitative implementation evidence, high value for deployment planning. |
| Surgical video AI | NEJM AI published a computer-vision study that used laparoscopic cholecystectomy video to identify surgical actions associated with blood loss and surgical experience (Aklilu et al., 2024). A 2026 NEJM AI appendectomy cohort extended this line of work by using swarm learning to train patient-level surgical-video disease-staging models across institutions without centralizing video data (Saldanha et al., 2026). | Low to moderate: promising analytic methods for local and privacy-preserving multicenter validation, not clinical intervention trials. |
The practical lesson is not that robotic surgery is good or bad. The lesson is that the unit of evidence is the procedure-institution-surgeon combination. A claim from rectal cancer surgery at expert centers should not be transferred to acute-care cholecystectomy, ventral hernia repair, hysterectomy, or community urology without local evidence.
Creating evaluations for robotic surgery
Every robotic surgery evaluation should begin with a falsifiable claim:
- Clinical outcome claim: robotic surgery reduces complications, conversion, recurrence, readmissions, pain, length of stay, or reoperation.
- Operational claim: robotic surgery improves OR throughput, case scheduling, turnover, staffing efficiency, or surgeon ergonomics.
- Training claim: robotic analytics improve skill acquisition, feedback quality, or credentialing reliability.
- AI claim: a model detects phases, instruments, anatomy, errors, risk states, or quality signals accurately enough to change decisions.
- Autonomy claim: a system performs a bounded physical subtask safely under defined oversight and abort conditions.
Do not evaluate “robotic surgery” as a generic intervention. Evaluate one claim at a time.
| Layer | Core question | Minimum evidence |
|---|---|---|
| Platform safety | Does the robot perform as intended under the cleared indication? | FDA record, device training requirements, malfunction reporting plan, local incident tracking. |
| Surgeon performance | Are operators past the learning curve for the procedure? | Case logs, simulation results, proctored cases, conversion and complication monitoring by surgeon. |
| Procedure outcomes | Does the robotic approach improve outcomes over the local comparator? | Procedure-specific outcomes, risk adjustment, comparable surgeon experience, 30-day and long-term outcomes when relevant. |
| AI perception | Does the model correctly detect instruments, anatomy, phase, smoke, bleeding, or errors? | Sensitivity, specificity, false alarms per hour, time-to-detection, external validation across surgeons and video systems. |
| Human factors | Do users understand when to trust, ignore, or override the system? | Simulation, silent-mode pilots, override audits, alert fatigue monitoring, qualitative workflow assessment. |
| Physical autonomy | Can the system act safely when tissue, lighting, bleeding, and anatomy vary? | Bench, simulation, ex vivo, animal, and eventually prospective clinical testing with predefined abort conditions. |
Match metrics to risk
Low-risk analytics, such as video indexing or case length prediction:
- Annotation accuracy
- Time saved in review or scheduling
- Inter-rater agreement with expert reviewers
- External validation across services
- User adoption and correction burden
Moderate-risk decision support, such as phase recognition or complication prediction:
- Sensitivity, specificity, PPV, NPV at local prevalence
- False alerts per case and per hour
- Time-to-detection before human recognition
- Calibration by procedure and patient subgroup
- Silent-mode performance before clinical use
High-risk intraoperative guidance, such as anatomy labeling or “do not cut” warnings:
- False negative rate for critical structures
- False positive rate causing unnecessary dissection delay
- Performance under blood, smoke, glare, lens fog, obesity, inflammation, adhesions, and variant anatomy
- Confidence display and uncertainty calibration
- Surgeon override and verification behavior
Physical action, including supervised autonomy:
- Task success rate and safety-margin violations
- Tissue trauma, force, thermal spread, bleeding, and clip or suture placement accuracy
- Recovery from near-miss states
- Human takeover latency
- Abort reliability
- Failure mode severity under worst-case scenarios
For irreversible actions, the evaluation threshold must be higher than diagnostic AI. A missed pulmonary nodule can still be reviewed. A divided bile duct cannot be undivided.
Use staged evidence, not one benchmark
The IDEAL framework for surgical robotics emphasizes development, comparative evaluation, and long-term monitoring rather than treating a single trial as the endpoint (Marcus et al., 2024). A practical hospital sequence is:
- Technical verification: confirm FDA indication, service contracts, instrument compatibility, downtime plan, and MAUDE reporting workflow.
- Simulation and dry-lab testing: test surgeon setup, docking, instrument exchange, emergency undocking, and AI display failure.
- Proctored clinical introduction: restrict to selected surgeons and procedures with clear exclusion criteria.
- Silent AI trial: run AI video analytics without clinical display, compare against expert annotation and outcomes.
- Limited visible pilot: display AI outputs to trained users, require manual verification, and audit overrides.
- Service-line deployment: monitor outcomes, costs, case mix, surgeon learning curves, and patient-reported outcomes.
- Post-deployment surveillance: review complications, conversions, reoperations, device malfunctions, video-model drift, and alert burden.
Skipping stages is most dangerous when AI moves from retrospective analytics to real-time intraoperative guidance.
Evals by use case
Surgical video analytics: Evaluate video analytics as a measurement system before treating them as a quality or safety intervention. Minimum evidence includes public and local test sets separated by surgeon, site, patient factors, and video system; inter-rater agreement for ground-truth labels; performance under blood, smoke, glare, lens cleaning, and off-axis camera views; error taxonomy; and prospective silent-mode validation before clinical display.
Robotic skill assessment: Automated performance metrics can reduce subjectivity in surgical education, but they must not collapse skill into speed or motion economy alone. A systematic review in the British Journal of Surgery found heterogeneous tools for robotic technical skills assessment and emphasized validity and reliability as central requirements (Boal et al., 2024). Minimum evidence includes correlation with blinded expert ratings, predictive validity for patient outcomes or supervised entrustment decisions, fairness across training level and prior robotic exposure, separation of technical execution from case complexity, and feedback that identifies remediable behaviors.
Anatomy labeling and warning systems: Anatomy labeling is high risk because confident false labels can create false reassurance. The safer near-term design is an uncertainty-aware warning system rather than an authoritative “this is the duct” display. Minimum evidence includes critical-structure false negative rates under worst-case visual conditions, confidence calibration, “unknown” states, stress tests with inflammation and variant anatomy, and explicit prohibition on irreversible action based on AI label alone.
Autonomous and semi-autonomous subtasks: Research is moving quickly. SRT-H used language-conditioned imitation learning for autonomous ex vivo cholecystectomy steps and achieved 100% success across 8 unseen pig gallbladders (Kim et al., 2025). A separate Science Robotics study introduced a surgical embodied intelligence simulator and demonstrated task autonomy across simulated, ex vivo, and in vivo animal settings (Long et al., 2025). These studies show meaningful progress in perception, planning, recovery, and sim-to-real transfer. They do not establish clinical readiness. Human surgery adds live bleeding, patient motion, anesthetic constraints, instrument failures, legal accountability, and rare anatomy that small experimental samples cannot resolve.
Procurement and governance
Robotic surgery programs should be governed like service-line investments. The evaluation should compare robotic surgery against the institution’s current best alternative, not against a theoretical average laparoscopic program.
Core local metrics:
- Case volume by procedure and surgeon
- Conversion to open surgery
- Intraoperative injury and bleeding
- Operative time, docking time, turnover time, and late-day delays
- 30-day complications, readmissions, emergency department returns, and reoperations
- Cancer-specific outcomes where relevant
- Patient-reported pain, function, urinary, sexual, and quality-of-life outcomes when relevant
- Direct and total costs, including instruments, disposable supplies, service contracts, staffing, depreciation, and OR time
- Training throughput and effects on laparoscopic competency
A 2025 systematic review of cost analyses in randomized trials found that robotic-surgery cost analyses are often incomplete, which means hospitals should not accept generic cost-effectiveness claims without local accounting (Bosscha et al., 2025).
Robotic surgery adoption can be shaped by marketing, patient demand, hospital competition, and industry relationships. The 2026 JAMA Network Open study linking industry payments to increased robotic-assisted surgery use does not prove inappropriate care, but it does justify governance around disclosure, credentialing, and value review (San Loh et al., 2026).
Robotic surgery committees should include surgery, anesthesia, nursing, sterile processing, biomedical engineering, finance, compliance, patient safety, and informatics. AI-enabled modules add model governance, data governance, and cybersecurity requirements.
Red flags:
- FDA status is unclear, misrepresented, or outside the intended use
- The vendor describes teleoperation as autonomy
- Outcomes are reported without comparator, case mix, surgeon experience, or learning-curve context
- AI analytics are trained on one institution’s videos and deployed elsewhere without external validation
- Anatomy labels are displayed without uncertainty or “unknown” states
- The system cannot export error logs, model version, video timestamp, and user override records
- Case volume is too low to maintain proficiency or amortize cost
- Training emphasizes platform operation but not failure recognition and emergency undocking
- Patient-facing marketing implies superior outcomes without procedure-specific evidence
No robotic-surgery AI should be trusted for irreversible intraoperative action without independent surgeon verification.
Postoperative AI Applications
Complication Prediction
Surgical Site Infection (SSI) Prediction:
ML models predict SSI risk using:
- Patient factors (diabetes, obesity, smoking, immunosuppression)
- Operative characteristics (duration, complexity, contamination class)
- Intraoperative variables (glucose control, normothermia, antibiotic timing)
- Postoperative factors (drain output, pain scores)
Evidence boundary: Prediction performance does not establish that acting on a score prevents infection. Prophylaxis or surveillance changes require a validated intervention pathway, not an AUC alone.
Postoperative Delirium:
Prediction models incorporating preoperative cognitive assessment, anesthesia factors, and postoperative medications identify high-risk patients for:
- Non-pharmacologic prevention (reorientation, sleep hygiene, family presence)
- Avoidance of deliriogenic medications
- Enhanced monitoring
Evidence boundary: Risk prediction must be separated from evidence that a linked prevention bundle improves outcomes.
Anastomotic Leak Prediction:
ML models analyzing postoperative labs (CRP trajectory), vital signs, and clinical notes can identify leak risk earlier than clinical suspicion alone.
Challenge: Low-prevalence outcomes require explicit calibration, alert-burden, and clinical-utility evaluation.
Deterioration Monitoring
AI systems analyze continuous vitals, laboratory trends, nursing documentation, and medication administration for deterioration risk. Prediction horizon and performance are model- and setting-specific.
Applications:
- Postoperative hemorrhage
- Respiratory failure
- Sepsis
- Cardiac events
External validation of a deterioration score does not establish benefit from deployment. A response protocol, alert-burden analysis, and prospective outcome evaluation are required (Wong et al., 2021).
Surgical Quality and Education
Video-Based Surgical Assessment
AI analysis of surgical videos enables objective skill assessment and quality improvement.
Applications:
Skill Scoring:
- Objective assessment of technical performance
- Identifies specific errors (tissue trauma, bleeding, inefficiency)
- Provides quantitative feedback for training
Evidence: AI scores correlate strongly with expert human assessment and predict surgical outcomes (Lavanchy et al., Scientific Reports, 2021).
Benefits for surgical education:
- Objective feedback supplements subjective faculty evaluation
- Tracks skill progression over time
- Identifies specific areas needing improvement
- Benchmarks against peer performance
Quality Improvement:
- Retrospective review of complications to identify technical factors
- Process improvement for OR efficiency
- Standardization of surgical techniques
Challenges:
- Privacy and medicolegal concerns about routine recording
- Surgeon resistance to surveillance
- Doesn’t capture decision-making quality (only technical execution)
- Storage and analysis infrastructure requirements
Natural Language Processing for Operative Notes
AI extraction of structured data from operative notes enables:
Quality Metrics:
- Automated calculation of process measures (antibiotic timing, VTE prophylaxis)
- Complication detection from dictated notes
- Adherence to defined surgical process measures
Registry Auto-Population:
- Reduces manual data entry burden for NSQIP, VASQIP, other registries
- Improves data completeness and accuracy
Clinical Decision Support:
- Extraction of critical operative details for downstream care (mesh type in hernia repair, prosthesis in joint replacement)
Evidence boundary: Extraction performance varies by element, institution, note template, and reference standard. Nuanced findings and judgment-based assessments require separate validation.
Selected Prospective Evidence
Perioperative hypotension prediction: The HYPE-2 randomized trial tested a machine-learning-derived Hypotension Prediction Index with diagnostic guidance during elective on-pump cardiac surgery and ICU care. Among 130 patients included in the primary analysis, the intervention reduced the median time-weighted average of MAP below 65 mm Hg by 63% and reduced time spent in hypotension by a median 28 minutes versus standard care (Schuurmans et al., 2025). The study supports protocolized hemodynamic decision support in cardiac anesthesia, but it was single-center and measured hypotension burden, not downstream complications or mortality.
Breast-Conserving Surgery
Claire (Perimeter Medical Imaging AI): FDA-Approved Intraoperative AI for Breast-Conserving Surgery
In March 2026, the Claire OCT System received FDA premarket approval (P250008) as an adjunctive intraoperative imaging tool for evaluation of excised lumpectomy margins during breast-conserving surgery (FDA PMA P250008, 2026; FDA SSED P250008, 2026).
- The FDA summary describes the wide-field optical coherence tomography system and its evaluated adjunctive workflow (FDA SSED P250008, 2026).
- In the 206-participant pivotal study, 35 participants had an unaddressed positive margin after standard care versus 28 after device-aided assessment, an absolute reduction of 3.4% and relative reduction of 20% that met the prespecified performance goal (P = .0050) (FDA SSED P250008, 2026).
- Clinicians using the device identified actionable residual disease in 14 of the 35 participants with residual disease after standard care. The reported 88.1% margin-level clinical-decision accuracy was a post hoc measure, not standalone device accuracy (FDA user manual P250008, 2026).
- The authorization includes a predetermined change control plan specifying permitted modifications and validation controls. It is not unrestricted permission to change the model.
Clinical significance: Claire does not replace standard histopathology; it provides adjunctive intraoperative information to guide margin assessment within the authorized workflow.
Critical Limitations and Risks
Immediacy of Harm: Unlike diagnostic errors that can be caught through physician review, intraoperative AI errors cause immediate, potentially irreversible patient harm.
Complexity of Surgical Judgment: Surgery requires integration of visual, tactile, and proprioceptive information with anatomical knowledge, pattern recognition from thousands of prior cases, and real-time adaptation to unexpected findings. AI doesn’t replicate this.
Medicolegal implications: Responsibility depends on the facts, applicable law, institutional policy, device labeling, and standard of care. Categorical liability predictions should not be inferred from the presence or absence of an AI output.
Technology Failure Modes: Computer vision fails with blood, smoke, optical artifacts. ML models fail with out-of-distribution inputs (unusual anatomy, rare findings). Risk models fail when patient circumstances differ from training data.
Trust Calibration: Surgeons must neither over-trust (following AI suggestions without verification) nor under-trust (ignoring useful AI alerts). Achieving appropriate calibration is difficult (Char et al., 2018).
Regulatory and Medicolegal Considerations
FDA Regulation of Surgical AI
FDA pathway and device class depend on the product’s intended use, risk, technological characteristics, and applicable predicate or statutory pathway. Labels such as planning software, navigation, risk calculator, or robot do not determine regulatory status by themselves. Before clinical use, verify the exact device record, authorization pathway, indication, inputs, users, and required oversight. The product-specific examples earlier in this chapter illustrate why broad class assignments are unsafe.
Medicolegal Principles
Key documentation practices include:
- Informed consent should mention AI use when material to patient decision
- Documentation should note AI tools used and how output was interpreted
- Documentation of independent verification and clinically material disagreement
Liability Boundary
No universal liability rule can be assigned to following or overriding an AI output. Institutions should obtain jurisdiction-specific legal and risk-management advice and preserve auditable documentation of the tool, version, output, clinical review, and action.
Evidence-Based Guidelines for Surgical AI Adoption
Before Adopting Any Surgical AI:
- Demand evidence: Prospective validation studies in diverse populations, not just retrospective accuracy metrics (Nagendran et al., 2020)
- Understand training data: Was the model trained on cases like yours? (Procedure types, patient populations, institutional practices) (Beam & Kohane, 2018)
- Know the failure modes: How does the system fail? What are the error rates? What happens with unusual cases? (Vabalas et al., 2019)
- Assess workflow integration: Does this fit your existing workflow or require disruptive changes?
- Clarify liability: What does your malpractice carrier say about using this AI? What does hospital legal counsel advise?
- Verify regulatory status: Is this FDA-cleared? For what specific indication?
- Evaluate cost-effectiveness: Does the benefit justify the cost (both financial and cognitive/workflow burden)?
Safe Implementation Practices:
- Pilot testing: Start with low-stakes applications, expand carefully based on performance
- Parallel validation: Run AI alongside current practice, compare results before replacing current approach
- Defined oversight: Clear protocols for who reviews AI outputs and how discrepancies are resolved
- Incident reporting: Systems to capture AI errors or near-misses
- Ongoing validation: Monitor real-world performance, don’t assume initial validation persists indefinitely
- User training: Ensure all users understand AI capabilities, limitations, and appropriate use
- Informed consent: Discuss AI use with patients when material to their decision-making
Red Flags (Avoid These AI Systems):
- Claims of autonomous surgical decision-making
- Black-box models with no explanation of predictions
- Lack of prospective validation studies
- Vendors unwilling to disclose training data characteristics
- No mechanism for reporting errors or failures
- Regulatory status unclear or misrepresented
- Pressure to adopt without adequate evaluation period
Professional Society Guidelines on AI in Surgery
ACS educational programs and informatics activities provide professional context, but they should not be labeled as a product-specific clinical practice guideline. Device selection should remain tied to FDA labeling, procedure-specific evidence, local validation, and monitored outcomes.
No ACS or SAGES product-selection clinical practice guideline was identified in this review. Educational activity, committee work, and conventional risk calculators should not be presented as evidence for a particular surgical AI product.
Conclusion
Surgery is fundamentally a human activity requiring manual skill, real-time judgment, and adaptation to unique patient circumstances. AI can enhance the cognitive work surrounding surgery (risk assessment, planning, quality improvement) and may eventually provide useful intraoperative information. But the surgeon’s hands, eyes, judgment, and responsibility remain central.
The most successful surgical AI applications will be those that respect the complexity of surgery, acknowledge uncertainty transparently, augment rather than replace expertise, and prioritize patient safety over technological impressiveness.
Independent verification remains necessary before any irreversible surgical action.