The Hippocratic Oath primum non nocere or "first, do no harm" applies to AI in healthcare as well. Our approach — Evidence-Based AI for Better Health™ — is based on the use of rigorous methodologies for the development, validation, transparent reporting, and safe deployment of AI algorithms. These algorithms support virtual coaching for patients' health behavior change, prevention, care alerting, diagnosis, prognosis, care planning, clinical decision-making, and remote patient monitoring.

The introduction of predictive models into clinical practice requires rigorous validation. In the context of supervised Machine Learning, dataset and covariate shifts can produce incorrect and unreliable predictions when the model training and deployment environments differ due to population, equipment, policy, or practice variations.

We follow existing consensus guidelines such as the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) AI Statement. Internal validation using methods like cross-validation or preferably the Bootstrap should provide clear performance measures such as discrimination (e.g., C-statistic or D-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). In addition to internal validation, external validation should be performed as well to determine the generalizability of the model to other patient populations. External validation can be performed with data collected at a different time (temporal validation) or at different locations, countries, or clinical settings (geographic validation). The clinical usefulness of the prediction model (net benefit) can be evaluated using decision curve analysis.

Traditional statistical validation approaches like cross-validation and the Bootstrap perform validation using data from the same clinical data set used for training the algorithm. This data set is typically collected during routine clinical care and stored in an electronic health records system or an imaging database. On the other hand, formal methods can generate counterexamples such as out-of-distribution (OOD) and adversarial inputs which can result in incorrect predictions. There is a growing literature on the use of formal methods based on probabilistic verification for providing provable guarantees of the robustness, safety, and fairness of Machine Learning algorithms.

Our approach supports a shared decision making process which takes into account the values, goals, and wishes of the patient. We see AI in healthcare as a tool which allows clinicians to focus more on providing care compassionately.

One lesson we have learned from studying the introduction of AI in medicine during the last decade is that the responsible use of AI requires not only validation and verification but also prospective studies to evaluate the efficacy of AI on patient-centered outcomes which include essential measures such as survival, time to recovery, severity of side effects, quality of life, functional status, remission (e.g., depression remission at six and twelve months), and health resource utilization. SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) Extension and CONSORT AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) Extension provide guidelines for the reporting of Randomized Control Trials (RCTs) of AI interventions.

In addition to Randomized Control Trials (RCTs) of AI interventions and the run-time monitoring of algorithms (e.g., for detecting distribution shifts), we believe that post-marketing surveillance is also required for ensuring patient safety.

We pay special attention to important issues such as: AI safety, security, privacy, human factors, algorithmic fairness, explainability, and accountability. To solve these issues, we are fully committed to long term research and development, methodological rigor, formal verification, and clinical validation.