Congratulations to Madeline Sagona, Graduate Academy Fellow, On Peer-Reviewed Publication with Mentors
A research publication authored by Hopkins Business of Health Initiative (HBHI) Graduate Academy Fellow Madeline Sagona and co-authored by HBHI leadership faculty Tinglong Dai, Mario Macis, and Michael Darden has just been published in Nature Portfolio Health Systems.
Sagona is a Genetic Epidemiology Master of Science Candidate at the Bloomberg School of Public Health. The article, titled “Trust in AI-assisted health systems and AI’s trust in humans,” is her first peer-reviewed publication.
“The idea came from something we kept seeing—every panel on AI and healthcare eventually turns into a conversation about trust,” said Dai. “So we asked: trust by whom, in whom, and for what?”
The article explores how the issue of trust, already a persistent theme in health care, raises to an even more critical level and offers a framework that will allow researchers and practitioners to think more clearly about trust in an AI-assisted world.
Below is an excerpt from the publication’s introduction:
Artificial intelligence (AI) is reshaping healthcare, promising improved diagnostics, personalized treatments, and streamlined operations. Yet a lack of trust remains a persistent barrier to widespread adoption. This Perspective examines the web of trust in AI-assisted healthcare systems, exploring the relationships it shapes, the systemic inequalities it can reinforce, and the technical challenges it poses. We highlight the bidirectional nature of trust, in which both patients and providers must trust AI systems, while these systems rely on the quality of human input to function effectively. Using models of care-seeking behavior, we explore the potential of AI to affect patients’ decisions to seek care, influence trust in healthcare providers and institutions, and affect diverse demographic and clinical settings. We argue that addressing trust-related challenges requires rigorous empirical research, equitable algorithm design, and shared accountability frameworks. Ultimately, AI’s impact hinges not just on technical progress but on sustaining trust, which may erode if biases persist, transparency falters, or incentives misalign.
“This is exactly the kind of partnership we want to see between our Graduate Academy Fellows and the mentors in the program,” said Dan Polsky, Director of HBHI. “Congratulations to Madeline for her fine work and thank you to our HBHI leadership team for your impactful engagement advancing the field of health care with our Graduate Academy fellows.”