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Artificial-intelligence models can estimate some risks before cardiac surgery as well as, and sometimes better than, conventional scores—but they are not ready to replace established calculators for every patient or hospital. Published comparisons report higher or similar discrimination for machine-learning models, yet many lack strong external validation, calibration testing, prospective trials, or evidence that using the model improves care.
The available literature also does not verify a publication, interview, institutional biography, or direct quotation connecting Dr. Barbara L. Robinson to a specific explanation of AI-based preoperative cardiac-surgery assessment. The evidence-based guide below therefore explains what the technology does, where it helps, and what patients and clinicians should verify rather than attributing these points to her.
Can AI predict cardiac-surgery risk better than current scores?
Sometimes, but the answer depends on the outcome, patient population, hospital, algorithm, and validation method. A 2026 systematic review in the Journal of Surgical Research compared artificial-intelligence models with traditional risk scores in 13 studies. The studies ranged from 308 to 647,726 patients, and reported area-under-the-curve (AUC) differences of 0.006 to 0.42 in favor of AI models in some comparisons. Those figures span different operations, endpoints, datasets, and study designs; they are not a pooled improvement or a guarantee for an individual operation.
Traditional tools remain central because they are familiar, routinely available, and linked to large clinical registries. The Society of Thoracic Surgeons (STS) says its Adult Cardiac Surgery Database (ACSD) Operative Risk Calculator estimates operative mortality, major morbidity, and short-term outcomes for most adult cardiac surgeries using the latest nationwide ACSD data. STS says its models are updated every three months.
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The practical conclusion is not “AI versus scores.” A safer interpretation is that a validated machine-learning model may add information to a conventional score, while clinicians still need to check whether its predictions are calibrated for their patients and whether it has been tested in their setting.
What does preoperative cardiac-surgery risk assessment actually measure?
Risk assessment converts a patient’s clinical information into estimates for outcomes such as death during or shortly after an operation, stroke, kidney injury, prolonged ventilation, infection, or extended intensive-care treatment. The estimate supports a discussion; it does not determine whether surgery is appropriate on its own.
What information goes into a model?
Inputs can include age, sex, body size, kidney function, diabetes, lung disease, previous operations, heart function, urgency, the planned procedure, and other diagnoses. A model may also use laboratory results, vital signs, imaging-derived measurements, medication history, and hospital-level or time-related variables. The exact list differs by model, and a variable that is available in one health system may be missing or defined differently in another.
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Most tools produce a probability for a defined endpoint, such as predicted operative mortality. Some produce several probabilities at once. A prediction is meaningful only when the endpoint, time window, and patient group are stated: “30-day mortality after isolated bypass surgery” is not interchangeable with “in-hospital mortality after any adult cardiac operation.”
How are AI models different from traditional cardiac-surgery scores?
Traditional scores usually apply a published set of variables and coefficients. Machine-learning systems can model nonlinear relationships and interactions among many variables, sometimes using free-text, waveform, imaging, or longitudinal electronic-record data. That flexibility can improve ranking of patients in a particular dataset, but it can also make the model harder to audit and more sensitive to changes in documentation or case mix.
| Comparison point | Traditional score or calculator | Machine-learning model |
|---|---|---|
| Typical data structure | Prespecified clinical variables and coefficients | May combine many structured or unstructured variables and complex interactions |
| Main strength | Transparent inputs, established workflow, and recognizable interpretation | Can capture nonlinear patterns that a fixed score may miss |
| Key risk | May perform less well when populations or treatments change | Overfitting, hidden bias, missing-data effects, and limited explainability |
| Evidence to check | Current version, population, endpoint, and local calibration | All traditional checks plus external and temporal validation, reporting quality, and reproducibility |
| Clinical role | Baseline comparator and one input to counseling | Potential supplementary estimate, not an automatic replacement |
What did the 2026 reviews find about AI in cardiac surgery?
The reviews show promise but also a substantial evidence gap.
Comparative performance
The Journal of Surgical Research review found that several AI approaches matched or exceeded traditional scores on discrimination. However, an AUC measures how well a model ranks patients from lower to higher observed risk. It does not show whether a predicted 10% risk actually occurs in about 10% of similar patients, nor whether displaying the estimate changes treatment or outcomes.
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Validation and reporting
A separate 2026 systematic review by Ha and Vohra found external validation in 26% of included studies, representing 47 studies. Only 19%, or 34 studies, followed TRIPOD or TRIPOD-AI reporting standards. External validation tests a model on data that were not used to develop it, preferably from another hospital or health system. Reporting standards make it easier to identify the population, missing data, predictors, outcome definition, model specification, and performance estimates.
Why those percentages matter
Without external validation, an impressive result may reflect local documentation habits or an unusually homogeneous cohort. Without transparent reporting, another team may be unable to reproduce the model, check for subgroup errors, or update it when surgical techniques and treatment pathways change.
Why are discrimination and calibration both necessary?
Discrimination asks whether higher-risk patients generally receive higher scores than lower-risk patients. Calibration asks whether the numerical probabilities match what actually happens. A model can rank patients correctly while consistently underestimating or overestimating the absolute risk.
For a patient deciding between treatment options, calibration is critical. If a tool says the risk is 2% when comparable patients experience the outcome about 8% of the time, the ranking may still look good while the counseling estimate is misleading. Evaluation should therefore include calibration plots, calibration slope or intercept, observed-to-expected ratios, confidence intervals, and performance across clinically important subgroups—not just one AUC.
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The STS ACSD Operative Risk Calculator remains an important reference point. Its current terms page identifies version 2.0.7, last updated July 26, 2026, and says the models are refreshed every three months. It covers mortality, major morbidity, and short-term outcomes after most adult cardiac surgeries.
STS explicitly limits the role of its estimates. Its official terms state: “STS risk calculator estimates should be considered as only one of several sources of information in cardiothoracic decision-making and patient counseling.” STS also cautions that the calculator should never be used to exclude a patient from surgery because of higher risk associated with a single characteristic. Clinician judgment and patient and family preferences remain part of the decision.
The American Association for Thoracic Surgery also describes a patient-specific cardiac-surgery risk calculator using machine-learning models and clinical data from its Quality Gateway program. The available description does not provide enough validation or outcome detail to rank it against the STS calculator. It is best treated as another service whose methods and local performance should be examined before use.
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What does external validation show about local performance?
A 2026 study evaluated EuroSCORE II, additive EuroSCORE, and the STS predicted risk of mortality in 1,486 patients at a single cardiac-surgery center in Colombia located 2,600 meters above sea level. In-hospital mortality was 7.9% (117 of 1,486 patients). The established scores could discriminate between patients with different levels of risk but underestimated mortality in that cohort.
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The study could not determine whether altitude, differences in case mix, local treatment practices, or another factor caused the miscalibration. Its lesson is broader than geography: a score that performs acceptably where it was developed can require recalibration or cautious interpretation elsewhere. The same check applies to an AI model trained at a tertiary referral hospital when it is deployed in a community hospital, a different country, or a service with different operative thresholds.
How is an AI risk estimate used before an operation?
A responsible workflow treats the model as decision support, with a clinician checking the inputs and explaining uncertainty.
- Define the decision and endpoint. Specify the operation, urgency, and outcome being estimated, along with the time window.
- Verify the input data. Confirm values such as creatinine, ejection fraction, prior surgery, and procedure type. Correct stale, duplicated, or implausible entries before calculating risk.
- Run a recognized comparator. Calculate the applicable STS or other established score so the team can see whether the AI estimate is materially different.
- Review model provenance. Check the development population, date range, outcome definitions, missing-data method, subgroup performance, and whether external and temporal validation were performed.
- Examine calibration and uncertainty. Look for calibration results in a population resembling the patient, prediction intervals or confidence intervals, and any warning for out-of-distribution cases.
- Discuss options with the patient. Explain that the number is an estimate, describe important benefits and harms, and include the patient’s goals and acceptable trade-offs.
- Document the reasoning. Record the model version, inputs, estimate, clinical interpretation, and why the final plan may differ from the numerical prediction.
What should a patient ask about an AI risk score?
- What exact outcome and time period does this percentage describe?
- Was the model tested in patients like me, at this hospital, and for this operation?
- How does its calibration compare with the usual STS or other established score?
- What information was missing or uncertain in my record?
- Would the estimate change if the operation were performed urgently, at another center, or with a different technique?
- How will this number affect the options being offered, and what clinical judgment could override it?
What can make an AI cardiac-surgery prediction unreliable?
Changes in patients or practice
New devices, surgical techniques, referral patterns, and perioperative treatments can shift the relationship between predictors and outcomes. A model trained before such changes may drift even when its software has not changed.
Incomplete or inconsistent records
Missing laboratory values, different coding practices, and copied-forward notes can create a false signal. A model may interpret “not recorded” as if it meant “normal” unless missingness was handled correctly.
Black-box explanations
Feature-importance displays can indicate which variables influenced a prediction, but they do not prove causation. A high-risk estimate may reflect a proxy for access to care, referral patterns, or documentation style rather than a modifiable biological mechanism.
Unequal subgroup performance
Overall accuracy can conceal errors in smaller groups, including patients with unusual conditions or different demographic and geographic characteristics. Performance should be reported by age, sex, race or ethnicity where appropriate, procedure type, urgency, comorbidity, and hospital type.
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Automation bias
Clinicians may give an algorithm more weight than contradictory bedside evidence. Requiring an explicit review of inputs and a documented clinical rationale helps keep the estimate subordinate to professional judgment.
What evidence should a hospital require before adopting a model?
A hospital should not adopt a model solely because its AUC is higher in a publication. A practical governance review should require:
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- Independent external validation and, when possible, temporal validation on recent local data.
- Calibration results, clinically relevant decision-curve or net-benefit analysis, and subgroup performance.
- Transparent reporting consistent with TRIPOD or TRIPOD-AI, including handling of missing data and model updating.
- Security, privacy, access controls, audit logs, and a plan for monitoring performance after deployment.
- Prospective evaluation of whether clinicians use the output appropriately and whether patient outcomes, equity, or workflow improve.
- A defined process for suspending the model when data quality or performance deteriorates.
The 2026 comparative review specifically called for prospective, multicenter validation before clinical adoption. That standard is especially important when an algorithm would influence consent, operating-room allocation, referral, or whether a patient is offered surgery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can patients trust an AI score before heart surgery?
Patients can reasonably treat a validated score as one piece of information, not as a verdict. Ask the team to identify the outcome being predicted, compare the result with an established calculator, and explain how well the model has worked in similar patients at that center.
A percentage cannot capture every factor that matters, including recovery priorities, caregiver support, frailty not represented in the model, or the consequences of delaying treatment. The most useful conversation combines the estimate with the surgeon’s and anesthesiologist’s assessment, alternatives, uncertainty, and the patient’s own goals.
What is the likely next step for AI in cardiac-surgery assessment?
The near-term role is more likely to be augmentation than replacement: identifying high-risk patterns, standardizing data review, prompting missing information, and presenting several validated estimates together. Progress will depend less on producing another algorithm and more on proving that a model remains calibrated across hospitals, is understandable to clinicians and patients, and improves decisions without widening disparities.
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Until those conditions are demonstrated, conventional scores such as the STS calculator remain necessary comparators. AI may sharpen a risk discussion, but it cannot substitute for validation, clinical expertise, or shared decision-making.
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Frequently asked questions
Does a higher AUC mean an AI model is safer for my operation?
No. AUC describes ranking performance, not whether the predicted percentage is accurate or whether using the model improves outcomes. Calibration, local validation, and prospective clinical evaluation are also needed.
Can a hospital change an AI risk score after surgery has been scheduled?
Yes. Risk can change with new test results, an urgent deterioration, or a change in the planned procedure. The team should record the model version and recalculate when clinically important inputs change.
Are traditional scores obsolete if machine learning performs better in a study?
No. Traditional scores provide a transparent benchmark and may be better validated in a particular health system. A machine-learning model should demonstrate added value in the same setting before it replaces that benchmark.
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Who is responsible if an algorithm gives an incorrect estimate?
The treating institution and clinicians remain responsible for clinical decisions; an algorithm is not an independent decision-maker. Hospitals should define oversight, auditing, escalation, and incident-review procedures before deployment.
Frequently Asked Questions
Does a higher AUC mean an AI model is safer for my operation?
No. AUC describes ranking performance, not whether the predicted percentage is accurate or whether using the model improves outcomes. Calibration, local validation, and prospective clinical evaluation are also needed.
Can a hospital change an AI risk score after surgery has been scheduled?
Yes. Risk can change with new test results, an urgent deterioration, or a change in the planned procedure. The team should record the model version and recalculate when clinically important inputs change.
Are traditional scores obsolete if machine learning performs better in a study?
No. Traditional scores provide a transparent benchmark and may be better validated in a particular health system. A machine-learning model should demonstrate added value in the same setting before it replaces that benchmark.
Who is responsible if an algorithm gives an incorrect estimate?
The treating institution and clinicians remain responsible for clinical decisions; an algorithm is not an independent decision-maker. Hospitals should define oversight, auditing, escalation, and incident-review procedures before deployment.
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