Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.
Can artificial intelligence (AI) help turn what many believe is clinical development’s biggest variable — and most prevailing bottleneck — into a more predictable process? In this episode of the Applied Clinical Trials podcast, Matt Walz, CEO of Trialbee, a patient recruitment company, joins Mike Christel, group managing editor, MJH Life Sciences, to discuss how AI is enhancing medical screening, clinical workflows, and enrollment planning.
The conversation opens with Walz placing FDA's recent call for feedback on its AI pilot program in early-stage research within a broader federal push to win back Phase I studies from China, where faster, cheaper processes and incentives for physicians to identify research candidates have given it an edge. He argues that industry's requests for clarity around use cases, metrics, and governance reflect a key distinction: AI applied to internal workflows, where a human can stay in the loop, is very different from AI-driven decision-making based on real-world data, where bias remains a concern.
Walz also shares how Trialbee is deploying AI on both the patient-facing and operational sides of its business, explains how conversational tools are helping teams move from dashboards to more specific answers, and discusses why he believes making enrollment predictable could be a game changer for drug development timelines and costs.
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