Trusting the Machines: Designing Transparent AI Solutions for Healthcare
Insights on agentic systems, compliance by design, and the future of interoperable intelligence
Alliance for Artificial Intelligence in Healthcare
Authors:
Paul Howard, PhD, Executive Director, Policy and Patient Experience Innovation, Amicus Therapeutics
Elaine Hamm, PhD, Executive Director, AAIH
With Contributions from:
Erik Huestis, JD, Partner, Foley Hoag LLP
Michael Patriarca, Vice President and Chief Business Officer, Tulane University School of Medicine
Mida Pezeshkian, PhD, Founder and Principal, STEMA_cg
Vangelis Vergetis, PhD, Co-founder, Epikast
Executive Summary
As AI systems become increasingly embedded in healthcare, the conversation is shifting. No longer are we simply asking whether AI can help. The question now is: how do we ensure we design AI systems that healthcare professionals and regulators can trust?
This write-up draws on insights from the panel “Trusting the Machines” at BIO 2025, featuring experts in regulatory strategy, pharma operations, system architecture, and data governance. Together, they explored how multi-agent systems, agentic AI, and human-in-the-loop design can advance not just innovation but accountability.
Conclusion
AAIH members have been building, testing, and deploying AI across the healthcare pipeline for more than a decade, from generative chemistry to postmarket surveillance. Their experience reinforces what this BIO 2025 panel made clear: trust is not a feature that can be added later. It is an outcome that must be designed into the system from the beginning.
Real progress in AI will not come from better models alone. It will come from better systems that are explainable, auditable, and aligned with how healthcare works. Trust emerges when there is clarity in how decisions are made, feedback between humans and machines, and shared standards for validation across evolving data and use cases.
As AI continues to evolve, AAIH is committed to:
Promoting shared infrastructure that supports both technical and regulatory interoperability.
Supporting member collaboration around real-world data, agent design, and validation frameworks.
Advocating for explainable, human-in-the-loop AI that meets the bar for scientific and regulatory trust.
Amplifying the work of members who are advancing new models of accountability and safety.
Encouraging incentive structures that reward data sharing and responsible innovation.
The future of AI in healthcare will not be determined by any single model, dataset, or company. It will be built through alignment between goals, tools, institutions, and people. AAIH is proud to help lead that alignment.