Health and Explainable AI Podcast

About Health and Explainable AI Podcast

The Health and Explainable AI podcast is a collaborative initiative between the Health and Explainable AI (HexAI) Research Lab in the Department of Health Information Management at the School of Health and Rehabilitation Sciences, and the Computational Pathology and AI Center of Excellence (CPACE), at the University of Pittsburgh School of Medicine. Led by Ahmad P. Tafti, Hooman Rashidi and Liron Pantanowitz, the podcast explores the transformative integration of responsible and explainable artificial intelligence into health informatics, clinical decision-making, and computational medicine.

Publisher

Pitt HexAI Lab and the Computational Pathology and AI Center of Excellence

Category

technology

Language

en

Explicit

No

First episode

6 Mar 2023

Latest episode

18 Jul 2026

Latest episodes

42 episodes in the feed.

1. 

   18 Jul 2026

   Sean McGunigal on Epic Software’s Role in Advancing the Frontier of AI in Healthcare

   In this special episode, HexAI producer Brent Phillips steps behind the microphone to speak with Sean McGunigal, Epic Software’s Software Development Lead for AI Research and Development. Drawing from his own daily experience navigating healthcare information management systems at a hospital, Brent guides a technical conversation on the immense role that enterprise software architectures like Epic play in orchestrating, optimizing, and embedding artificial intelligence directly into clinical workflows across global health systems. Bridging the gap between frontier software engineering and clinical realities, Sean and Brent discuss Epic's AI development pipeline while anchoring the conversation around Epic’s technical vision where utility, explainability, and trust are paramount. Sean details how Epic’s proprietary ambient AI framework, Chart with Art, seamlessly ingests and parses acoustic conversational data to generate structured documentation, effectively alleviating cognitive burden and clinical friction so medical professionals can prioritize hands-on patient care. Sean emphasizes that to achieve user adoption, Epic's generative summaries are rigidly tied to programmatic data lineage and granular citations, forcing black-box models to prove their clinical outputs. Aligning with the mission of the University of Pittsburgh’s Health and Explainable AI Research Laboratory and Pitt’s Computational Pathology and AI Center of Excellence (CPACE), the episode highlights Epic’s commitment to helping advance and democratize ethical and responsible AI engineering through open-source frameworks and massive data initiatives. Sean points to Nebula, Epic’s cloud-native platform deployed in Microsoft Azure, which provides a high-throughput, horizontally scalable infrastructure allowing health networks to securely deploy LLMs. Furthermore, he underscores Epic's breakthroughs with Cosmos, their multi-institution de-identified dataset, and Curiosity, Epic's custom-trained Medical Language Model designed to predict patient clinical events using autoregressive, next-token intelligence. By exploring the orchestration of complex agentic AI workflows, runtime evaluation harnesses, and cross-platform communication protocols, Sean closes the episode with a call to action for researchers to get involved and work more closely around safely advancing the frontiers of computational medicine.

2. 

   3 Jun 2026

   Beyond the Pixels: Dr. Bradley Erickson from the Mayo AI Lab on Medical Imaging and Radiology

   Dr. Bradley Erickson, Director of the Mayo AI Lab, speaks with HexAI podcast host, Jordan Gass-Pooré in advance of the University of Pittsburgh’s annual AI Summer School program in Medical Imaging Informatics organized by Pitt's Health and Explainable AI Research Lab (HexAI) and the Computational Pathology and AI center of Excellence (CPACE). The episode simulates two different professional vantage point scenarios to help students visualize the vast, multi-dimensional landscape of artificial intelligence in healthcare and radiology. The first half of the episode drops students directly into the vantage point of an AI expert attending a technical conference, where medical imaging informatics are being contrasted with everyday computer vision. Dr. Erickson explains how medical data often extends into multiple dimensions by incorporating complex spatial matrices and tissue properties like T1 and T2 tracking on MRIs, far surpassing standard 2D photographic pixels. He highlights why generic consumer AI tools like simple heat maps or saliency maps fall short of establishing clinical trust; while they can successfully point to where a brain tumor is, they completely fail to explain what that tumor is or why it is changing texture. Furthermore, Dr. Erickson discusses the profound challenge of "ground truth" uncertainty in medicine, explaining that training predictive algorithms is incredibly difficult because definitive biological labels are frequently masked by biological reactions or a lack of definitive longitudinal data. The second half of the podcast episode places students into the role and vantage point of a hospital administrator, exposing students to the active economic and structural deliberations currently playing out in modern hospital boardrooms. Dr. Erickson underscores the considerations and financial constraints that hospitals contend with and explains that while new narrowly focused diagnostic AI tools are attractive, the most immediate return on investment for hospitals often comes from practical, language-based text summarization and ambient patient recording systems. Crucially, this administrative perspective teaches students that the health industry desperately needs supportive roles beyond traditional doctors and researchers, such as AI project managers, integration specialists, and governance officers who can oversee model confidence and decide exactly when to adapt AI solutions or pull failing applications or algorithms back. Dr. Erickson emphasizes that entering this revolutionary field requires a willingness to learn through iteration, push back on assumptions, and manage the critical intersections of technology, safety, and human care. Through an open exploration of technical hurdles and administrative realities, the episode provides a rich conceptual primer for AI Summer School participants designed to cultivate critical thinking informing views on AI in medical imaging, hands-on project development and coding.

3. 

   14 May 2026

   Innovating Precision Medicine with Dr. Freddy Nguyen

   Dr. Freddy Nguyen, a physician-scientist-entrepreneur and Director of MIT’s Catalyst Scholars Program, discusses his work at the frontier of translational research, diagnostics, precision medicine and healthcare innovation with Pit HexAI host Jordan Gass-Poore' and his involvement in co-founding Nine Diagnostics, a startup spun out of Memorial Sloan Kettering Cancer Center. Focusing on innovation in precision medicine, Dr. Nguyen traces his path through initiatives like MIT Hacking Medicine and the MIT Catalyst Scholars Program and his work helping teams identify and turn real clinical problems into projects designed to reach patients. Emphasizing patient‑first and science‑first approaches to innovation, Dr. Nguyen encourages students and collaborators to ask why things work the way they do and to build solutions that can move quickly from lab to clinic. That same mindset underpins Nine Diagnostics, which uses a high‑throughput nanosensor platform to generate molecular “fingerprints” of disease. Instead of tracking a few isolated biomarkers, these fingerprints capture complex patterns across thousands of molecules, reflecting both tumor biology and the broader physiological context of each patient. This shift from genomics alone to “functional precision medicine” enables clinicians and researchers to see what is happening in real time inside the body, monitor treatment response faster and tailor therapies more precisely to each patient. Touching on how AI and machine learning are making these technologies clinically useful, Dr. Nguyen discusses how advanced algorithms integrate multimodal data streams to discover patterns that would be impossible to detect by eye. These models not only improve sensitivity and specificity when predicting treatment response, but also support emerging “digital twin” computational representations of patient health that can be used to simulate and optimize care. At the same time, he emphasizes that more data is not automatically better, and that explainable AI in healthcare must focus on which signals truly matter for a specific clinical decision and how to close the loop between model outputs and underlying biology. For students and early‑career researchers, Dr. Nguyen shares practical guidance on getting involved in leveraging AI to advance precision medicine and designing research with translation in mind from day one so that innovations reach patients faster, rather than staying trapped in academic silos.

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

4 ratings

Spotify

5.0 / 5

5 ratings

Podcast Authority Score: 53 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

73

Social presence

0

YouTube

0

Engagement

81

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Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.