Women's Health World Conference 2026

Abstract

Unlike traditional medical devices, AI-enabled tools evolve continuously, meaning their safety and performance can shift over time. This presentation introduces a five-stage lifecycle framework to guide clinicians in critically evaluating and safely adopting AI medical devices — particularly in the context of women's health.

The framework covers:
(1) Model Design, which addresses intended use, clinical purpose, and the degree of automation
(2) Validation & Evidence, emphasizing both technical accuracy and real-world clinical utility through performance metrics like sensitivity, specificity, and AUROC
(3) Deployment & Workflow Integration, focusing on human factors, cognitive load, and seamless integration into clinical decision-making
(4) Monitoring & Real-World Performance, including tracking data drift, false positives/negatives, and establishing governance pathways
 (5) Updates & Change Control, ensuring algorithm changes are transparently communicated, validated, and safely deployed.

The presentation also highlights critical cross-cutting issues including bias and equity (ensuring AI systems are tested and validated across diverse demographic groups), cybersecurity and data stewardship, and liability and clinical oversight clarifying where responsibility lies when AI recommendations are incorrect.

A practical implementation checklist is provided to help clinicians ask the right questions before adopting any AI tool, covering training data relevance, bias monitoring, workflow fit, performance reliability, update notifications, security measures, and liability clarity.