The next phase of AI in healthcare is about building systems where human clinicians, supervisors, and AI can all contribute, challenge each other, and improve how decisions are made.
AI doesn't just demand better processes. It requires a complete redesign of traditional healthcare operations for a new level of precision, traceability, and governance. A new operating model that the vast majority of healthcare organizations simply aren't built around today.
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Will doctors or patients who are burned by one AI solution trust the next one they’re given? Probably not. That’s why every provider rolling out AI tools has to understand this risk and build governance into its development process.
AI in healthcare will not fail because the models are weak. It will stall when leaders hesitate to redesign how decisions are made, measured and governed.
GenAI has real promise, but it’s also bringing real risks. It is not about whether we should use it but how we can use it responsibly and with positive outcomes.
We should never wait for tragedies to force a conversation around clear governance and accountability. Oversight has to evolve alongside innovation to protect people before harm occurs.
Discover the top AI-powered EAP providers for 2026. Compare platforms like Kyan Health and Spring Health on triage speed, global reach, and clinical quality to transform workforce wellbeing.
At MedCity News’ INVEST Digital Health conference, healthcare experts explored strategies to mitigate automation bias — emphasizing the importance of vendor responsibility, use case-specific governance and clinician engagement.
Without trust, the rapid adoption of AI in healthcare could stall, pointed out Joel Gordon, UW Health’s chief medical information officer. He urged healthcare leaders to focus less on flashy rollouts and more on governance, collaboration, and meaningful metrics to ensure AI delivers lasting value.