Healthcare AI promises to transform patient care, reduce costs, and save lives. Yet 79% of healthcare AI projects fail to deliver value. The gap between AI potential and healthcare reality is massive—driven by data privacy constraints, clinical validation requirements, and EHR integration challenges unique to healthcare.
Frequently Asked Questions
The 79% failure rate in healthcare AI stems from poor data quality, lack of clinical workflow integration, unrealistic expectations, insufficient change management, and regulatory complexity. Successful projects start with clearly defined clinical problems and strong physician involvement.
Common mistakes include deploying AI without clinician buy-in, using biased or incomplete training data, ignoring existing clinical workflows, underestimating regulatory requirements, and failing to validate AI outputs against clinical outcomes.
Start with well-defined clinical use cases, ensure high-quality data pipelines, involve frontline clinicians from day one, build governance frameworks before deployment, and measure outcomes against specific clinical KPIs rather than general AI metrics.