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Aidoc Staff

What Real AI Adoption Looks Like Inside Two Health Systems

Adoption, aiOS, Information Technology, Workflow
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In a recent webinar, Avi Sharma, MD, CIIP,  Jefferson Health, and Leonardo Kayat Bittencourt, MD, PhD, University Hospitals, shared what AI implementation actually looks like in practice, including the decisions and lessons that don’t usually make it into case studies or news releases.

Moderated by Tom Valent, Aidoc’s Chief Business Officer, the conversation explored the realities of deploying AI across complex health systems — from cross-specialty workflows and user-level adoption to governance models that can keep pace with rapid change.

What Stood Out During the Discussion

1. The strongest signal that AI adoption is working? When clinicians notice it’s missing.
Dr. Bittencourt shared an anecdote about a brief system outage during which multiple radiologists flagged the absence of AI support — not because it failed but because they had come to expect it. The spike in nonengagement alerts during that window underscored how deeply the tool had embedded itself into clinical practice.

2. Radiology must lead — or risk being sidelined.
Both speakers emphasized radiology’s critical role in enterprise AI strategy. With vendors increasingly marketing directly to downstream teams, Drs. Sharma and Bittencourt argued that radiologists need to leverage their deep knowledge and experience with AI to proactively help shape implementation, governance and cross-specialty coordination.

3. There’s no such thing as a one-size-fits-all workflow.
At University Hospitals, AI was integrated in a way that allowed for user-level customization. Some radiologists preferred alert-based widgets; others used PACS-driven prioritization. The ability to support both was a key factor in widespread adoption and an argument for systems to explore a platform that offers flexibility, not just features.

4. Standardization has limits — especially across hospitals.
Dr. Sharma noted that even within a single system, deployment must account for local workflows, staffing models and data environments. The shift from pilot to platform, he said, required more than technical integration — it demanded orchestration, clinical alignment and governance that could scale.

5. Governance is still evolving, and that’s OK.
Both Jefferson Health and University Hospitals are still refining their governance structures. Dr. Sharma shared how Jefferson Health’s AI committee expanded from a radiology-specific body to a multi-hospital enterprise steering group. Dr. Bittencourt described a tiered structure that includes research, clinical operations and executive oversight with radiology at the table throughout. If you’re building a governance framework, coordinating cross-specialty AI workflows or planning to scale AI beyond radiology, the experiences shared in this webinar could spark inspiration. Access the recording.

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