Q: What is the central idea behind how to evaluate healthcare AI? A: The central idea behind how to evaluate healthcare AI is to judge an AI tool as part of the human‑machine team rather than rely on headline accuracy alone. Focus on how the model and clinicians perform together by measuring outcomes, behavior, and context, and keep checking performance over time. Q: Which metrics matter most when assessing a clinical AI tool? A: Key metrics include discrimination (sensitivity/recall, specificity, precision/PPV, NPV, ROC‑AUC/PR‑AUC) and calibration, as well as coverage and timeliness. Also measure burden (alerts per patient/day, false positives per true positive, time‑on‑task), net benefit via decision curve analysis, and cost impact such as cost per true positive and avoided tests. Q: What study designs should hospitals use to mirror real clinical work? A: Use retrospective external validation on recent, local data, clinician simulation with scenarios and time pressure, silent‑mode (“shadow”) deployments, pragmatic trials (stepped‑wedge, cluster RCT, or A/B testing), and post‑deployment monitoring with drift detection and safety audits. Plan a path from bench to shadow to limited go‑live to scale and set clear stop/go rules at each step. Q: How should teams measure human‑machine interactions like trust and overrides? A: Measure adoption (who sees alerts, who acts, and how fast) and override rates to understand when users ignore or dismiss alerts and why. Also track automation bias, algorithm aversion, and decision concordance to see whether AI changes choices toward guidelines or expert review. Q: What reliability and safety measures should be in place for deployed AI? A: Monitor data drift by watching input distributions and label shifts and track performance drift with rolling sensitivity, PPV, and calibration, setting thresholds that trigger review. Establish an update policy with required tests and rollback steps, safe defaults and double‑checks for high‑risk actions, rapid incident logging and root‑cause analysis, and a kill switch with a clear owner to pause the model instantly. Q: How do you check generalization and equity before scaling an AI tool? A: Test across different hospitals, scanners, labs, devices, and EHR versions and evaluate performance across subgroups such as age, sex, race/ethnicity, language, and clinical conditions. Compare error rates, calibration, and PPV for fairness, assess whether the tool will widen or narrow care gaps, and publish limits or add guardrails or tailored thresholds where performance drops. Q: What should hospitals ask vendors during due diligence for clinical AI? A: Request documentation of intended use, target population, contraindications and known failure modes, data sources and labeling quality, and external validations with sample sizes, plus a model card that lists metrics, subgroups, calibration, uncertainty, and limits. Also insist on a change log and update plan with versioning and rollback procedures, security and privacy details, regulatory and post‑market surveillance status, SLAs and support, and ask to see raw confusion matrices and calibration plots rather than only a glossy AUC. Q: What practical steps are on the launch checklist for a new AI alert? A: Define one decision, one owner, and one primary outcome, measure a clean baseline for 3–6 months, and run silent mode for 2–4 weeks to compare to baseline with pre‑specified acceptance gates for PPV, sensitivity, calibration, alert rate, and subgroup floors. Start a limited go‑live with training and live support, hold weekly reviews to adjust thresholds and UI, keep visible dashboards for performance and drift, scale gradually, and re‑validate after major system shifts.