Healthcare Providers Need AI Strategy, Not Experimentation
Healthcare organizations should adopt AI through deliberate strategy rather than trial-and-error, according to Dr. Bill Fera, genAI leader for life sciences and healthcare at Deloitte Consulting. Providers will only trust AI tools if they understand how those tools work within their workflows and why they produce specific outputs.
Transparency matters most. Healthcare systems need to audit AI performance so users can see what's happening inside the black box. Without visibility into how an AI system reaches a conclusion, clinicians won't adopt it-no matter how accurate it claims to be.
The IP Problem
A tension exists between protecting proprietary technology and providing the openness that healthcare professionals demand. "I don't think we've figured out how to solve for that tension yet, but we still have to keep pushing on it," Fera said.
Vendors face pressure to reveal enough about their models to satisfy auditors and clinicians, while keeping trade secrets intact. The healthcare industry hasn't settled on where that line should be.
What Matters for Implementation
- Establishing accountability frameworks so someone answers for AI decisions
- Validating AI results with clinicians before deployment
- Understanding AI's effect on workforce roles and responsibilities
- Recognizing that promising technologies fail without adoption-and adoption requires trust
The stakes are high. AI for Healthcare can reduce clinician burden and improve patient outcomes, but only if providers actually use it. That requires governance structures, human oversight, and the ability to explain what the system is doing.
For organizations building Generative AI and LLM applications in healthcare, the lesson is clear: move past the proof-of-concept phase with intentional planning around governance and trust.
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