Healthcare leaders have moved beyond hype about generative models to confront a harsher reality: data, not models, is the primary bottleneck to safe, effective AI. Electronic health records are fragmented, inconsistently coded and rife with documentation gaps and bias, producing training sets that degrade performance, obscure failure modes and limit generalizability across hospitals. Privacy, consent and proprietary vendor formats further constrain access, while incomplete labeling and poor provenance make clinical validation and regulatory review difficult.
Health systems, vendors and regulators therefore need to prioritize data strategy: standardized data models, robust governance, curated labeled datasets, better integration of clinician-in-the-loop annotation and ongoing post-deployment monitoring to detect drift and harm. Approaches such as federated learning, synthetic data and interoperable APIs can mitigate access and privacy barriers, but organizations must also invest in talent, tooling and outcome-driven validation to translate models into measurable clinical and operational value.
