HITLAB Launches Thinking AI Thursdays: Where Healthcare Leaders Separate AI Hype from Proof
New York, NY – February 4, 2026 – HITLAB today announced the launch of Thinking AI Thursdays, a high-impact weekly virtual series designed for healthcare leaders who are done with AI hype and need defensible answers. The complimentary 30-minute sessions will run every Thursday from 11:30 AM–12:00 PM ET, convening senior experts to tackle one urgent question: How do we know healthcare AI actually works—and when it doesn’t? Produced by HITLAB’s Center for AI Testing and Evidence (CAITE), Thinking AI Thursdays goes beyond vision and demos to focus on evidence, validation, governance, and real-world performance. Each session delivers practical frameworks and applied insights that healthcare executives, medical affairs leaders, regulators, and innovators can use immediately to evaluate AI risk, credibility, and impact. As AI rapidly moves from experimentation into clinical, operational, and financial decision-making, the consequences of getting it wrong are growing—patient harm, regulatory exposure, reputational damage, and wasted capital. Thinking AI Thursdays is designed to close the widening gap between AI enthusiasm and AI accountability.
Inaugural Session — Thursday, February 5
The series opens with Chrysanthi Dori, VP of Clinical Informatics at Medidata and Chair of CAITE, who will introduce CAITE’s AI Evaluation Framework—a practical approach for assessing whether AI solutions are: Clinically valid Operationally reliable Transparent and auditable Ethically and regulatorily defensible “AI in healthcare has reached an inflection point,” said Dori. “Innovation alone is no longer sufficient. Leaders need evidence they can stand behind—internally, clinically, and publicly. Thinking AI Thursdays is about giving decision-makers that confidence.”
What Participants Can Expect
Each weekly session is concise, rigorous, and grounded in real-world use cases, covering topics such as: How to evaluate AI performance beyond pilot studies Common failure modes in healthcare AI—and how to detect them early Evidence standards that resonate with regulators, payers, and boards Governance models that scale responsibly across health systems. No sales pitches. No buzzwords. Just substance.
- Healthcare and life-science executives
- Medical affairs and clinical leaders
- Digital health innovators and investors
- Policy, compliance, and regulatory stakeholders
- Participation is complimentary and open to the global healthcare community.
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