Health and Explainable AI Podcast

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

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.

  1. Jun 3

    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.

    31 min

  2. May 14

    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.

    28 min

  3. Apr 7

    George Demiris on Proactive Healthcare and The Future of AI in Nursing and Aging

    George Demiris, Associate Dean for Research and Innovation at the University of Pennsylvania School of Nursing and a “Penn Integrates Knowledge University Professor” discusses the transformative integration of responsible and explainable artificial intelligence into nursing, elder care, and hospice settings with Pit HexAI host Jordan Gass-Pooré. The University of Pennsylvania School of Nursing is actively integrating emerging technologies into its curriculum, research, and clinical practice to enhance person-centered care, ensuring that technological advancements support rather than replace human connection, with the Penn Artificial Intelligence and Technology (PennAITech) Collaboratory for Healthy Aging playing a central role in bringing together interdisciplinary experts to address the technical and ethical challenges of integrating AI into the aging process. Discussing his work focusing on information technology's role in the healthcare of older adults, specifically through smart home solutions and passive sensing systems that support aging in place, George advocates for a shift from reactive to proactive care, using sensors for example to detect subtle behavioral changes before adverse events like falls occur. However he argues that technology must remain a "decision aid" rather than a final decision-maker, advocating for "self-reflective AI" that explains its reasoning to clinicians. This approach preserves the "moral agency" of nurses, who act as vital patient advocates ensuring AI tools are introduced ethically and reflect the diverse preferences of those they serve. Looking ahead, the conversation stresses the need for fluid collaboration between academia and industry to keep pace with rapid innovation. George envisions a holistic future for AI that prioritizes human dignity and autonomy, utilizing generative tools to adapt complex medical information to the specific literacy and language needs of patients and their caregivers.

    33 min

  4. Mar 18

    Martin Raison CTO of Nabla on Architecting the Agentic AI Era in Healthcare

    Martin Raison, Co-founder and CTO of Nabla speaks with Pitt HexAI host Jordan Gass-Pooré about Nabla’s central role in architecting the agentic AI era in healthcare. Martin details Nabla’s evolution from a specialized ambient scribing tool into a comprehensive "Adaptive Agentic Platform". They discuss the significant challenges involved in making it possible for AI agents to perform complex clinical tasks and how Nabla has been thrust into tackling a labyrinth of structural and data hurdles. These range from the integration of fragmented, unstructured patient charts and hospital guidelines to the complex technicalities of agent discoverability, interoperability, and the establishment of standardized accountability frameworks. The interview highlights a significant shift in Nabla's technical strategy: moving from probabilistic Large Language Models (LLMs) toward world models. Raison explains that while LLMs are effective at generating text, they lack a fundamental understanding of cause-and-effect and the ability to simulate evolving environments. To address this, Nabla has entered an exclusive partnership with Advanced Machine Intelligence (AMI), a research lab co-founded by Yann LeCun. This collaboration provides Nabla with early access to world model technologies that can "imagine" different scenarios and simulate the consequences of actions, providing a more deterministic and auditable path for AI in high-stakes clinical settings. In discussing the technical foundations of computational health, Martin addresses the critical need for inference optimization to manage the millions of model executions required daily at scale. Furthermore, Martin envisions a fundamental shift in the paradigm of AI inference through the adoption of world models. He suggests that these architectures will blur the traditional boundary between training and inference by enabling continuous learning, where the model adjusts and evolves in real-time based on new data and clinician feedback, rather than being limited by the static context windows of current LLMs. Beyond the core technology, Martin and Jordan discuss the critical importance of explainability and interoperability in the "agentic web" of healthcare. They specifically highlight architectural initiatives like MIT’s Project NANDA, which focuses on the foundational layers of the agentic web, including critical elements like discoverability and authentication that go beyond the AI layer alone. Martin emphasizes that the sector must move toward standardized "Agent Fact Files" to ensure accountability and ease of governance as organizations begin to manage thousands of agents. He concludes by looking toward a future of "emergent intelligence," where the collaboration between multiple models creates sophisticated patterns that can eventually help clinicians improve their own professional practice over time.

    38 min

  5. Feb 7

    Ekaterina Kldiashvili from the Tbilisi Medical Academy on Responsible Uses of AI, Medical Education and Inter-University Collaboration

    Ekaterina Kldiashvili, Vice Rector for Research at Petre Shotadze Tbilisi Medical Academy, and Pitt’s HexAI podcast host, Jordan Gass-Pooré, discuss public health, the incorporation of AI into healthcare, responsible uses of AI, medical education and inter-university collaboration. Ekaterina and Jordan explore opportunities and concerns surrounding commercial AI applications, noting that while AI can improve healthcare efficiency, it must support clinical reasoning rather than replace it. They cover the Tbilisi Medical Academy’s work on responsible AI usage, particularly in educating providers and patients, demonstrating how AI-enhanced text and visuals can significantly improve patient understanding and follow-up rates. They also touch on challenges associated with the use of AI in non-English languages like Georgian and delve into advances in computational genomics and rapid molecular diagnostics. Looking ahead, they discuss the strengthening ties between the University of Pittsburgh and the Tbilisi Medical Academy through knowledge sharing and faculty training and broadly discuss inter-university collaboration and the idea of seeing students investigate how different cultures and communities trust and accept AI in healthcare settings.

    28 min

  6. 12/18/2025

    Richard Bonneau from Genentech on Drug Discovery, Computational Sciences and Machine Learning

    Richard Bonneau, Vice President of Machine Learning for Drug Discovery at Genentech and Roche, provides Pitt’s HexAI podcast host, Jordan Gass-Pooré, with an insider view on how his team is fundamentally changing and accelerating how new drug candidate molecules are designed, predicted, and optimized. Geared for students in computational sciences and hybrid STEM fields, the episode introduces listeners to uses of AI and ML in molecular design, the biomolecular structure and structure-function relationships that underpin drug discovery, and how distinct teams at Genentech work together through an integrated computational system. Richard and Jordan use the opportunity to touch on how advances in the molecule design domain can inspire and inform advances in computational pathology and laboratory medicine. Richard also delves into the critical role of Explainable AI (XAI), interpretability, and error estimation in the drug design-prototype-test cycle, and provides advice on domain knowledge and skills needed today by students interested in joining teams like his at Genentech and Roche.

    30 min

  7. 11/19/2025

    Dennis Wei from IBM on In-Context Explainability and the Future of Trustworthy AI

    Dennis Wei, Senior Research Scientist at IBM specializing in human-centered trustworthy AI, speaks with Pitt’s HexAI podcast host, Jordan Gass-Pooré, about his work focusing on trustworthy machine learning, including interpretability of machine learning models, algorithmic fairness, robustness, causal inference and graphical models. Concentrating on explainable AI, they speak in depth about the explainability of Large Language Models (LLMs), the field of in-context explainability and IBM’s new In-Context Explainability 360 (ICX360) toolkit. They explore research project ideas for students and touch on the personalization of explainability outputs for different users and on leveraging explainability to help guide and optimize LLM reasoning. They also discuss IBM’s interest in collaborating with university labs around explainable AI in healthcare and on related work at IBM looking at the steerability of LLMs and combining explainability and steerability to evaluate model modifications. This episode provides a deep dive into explainable AI, exploring how the field's cutting-edge research is contributing to more trustworthy applications of AI in healthcare. The discussion also highlights emerging research directions ideal for stimulating new academic projects and university-industry collaborations. Guest profile: https://research.ibm.com/people/dennis-wei ICX360 Toolkit: https://github.com/IBM/ICX360

    25 min

  8. 10/14/2025

    Jason Moore from Cedars-Sinai on the Incorporation of AI Agents into Precision Health

    Jason Moore, Chair of the Department of Computational Biomedicine and Director of the Center for Artificial Intelligence Research and Education (CAIRE) at Cedars-Sinai Medical Center in Los Angeles, CA, speaks with Pitt’s HexAI podcast host, Jordan Gass-Pooré, about his work, the strategic investments his center is making in technology and specialized human expertise to support advanced AI research and about the incorporation of AI and AI agents into precision health. They speak in depth about the recent and rapid emergence of agentic AI, which is expected to have a significant impact on healthcare and how his team’s work is advancing the field. They also touch on vetting, deploying, and monitoring AI models for clinical use; explainable AI, trust, and transparency; using AI chatbots to improve the patient experience; the importance of building effective collaborations between industry and academia; and Cedar-Sinai’s new PhD program in Health AI.

    33 min
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Ratings & Reviews

5
out of 5
4 Ratings

About

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.

Information

  • Creator
    Pitt HexAI Lab and the Computational Pathology and AI Center of Excellence
  • Years Active
    2023 - 2026
  • Episodes
    41
  • Rating
    Clean
  • Copyright
    © Pitt HexAI Lab and the Computational Pathology and AI Center of Excellence
  • Show Website
    Health and Explainable AI Podcast

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