The 2026-2027 cohort of Freedman Fellows includes three professors whose expertise span different disciplines and colleges. Fellows will work with an assigned liaison from the Freedman Center and present their work at the annual forum in early fall.
Learn more about the program and see how to apply at this link.
Preparing Future Physicians for Patient Conversations About Health AI: A Community-Informed Approach
Project Lead: Lynette Hammond Gerido
Affiliation(s)
- Assistant Professor in the Department of Bioethics, School of Medicine
- Member, Population and Cancer Prevention Program, Case Comprehensive Cancer Center
Project Description
Patients increasingly use consumer AI and health technologies such as ChatGPT and Claude, as well as language translation tools, consumer genetic testing platforms, and continuous glucose monitors to interpret cancer risk, track family health history, and guide lifestyle modifications. Yet, patients lack guidance on the limitations, ethical considerations, and data stewardship challenges these tools present.
This project pursues two aims: (1) documenting how families affected by cancer use AI and consumer health technologies through qualitative family focus groups, and (2) creating an open-access course that raises public awareness of the limitations, ethical considerations, and responsible data stewardship practices associated with patient-initiated AI use. Working with medical student researcher. With the support of Olivia Masse (2nd year Medical Student) and Yash Mishra (Bioethics student research assistant), the project will recruit 3-5 families through patient advocacy organizations and genealogy societies, conducting focus groups that explore real-world patient technology practices. Thematic analysis of these conversations will inform both the public-facing course and a 30-minute interactive simulation featuring in the Case Western Reserve University Scholars Collaboration in Teaching and Learning (SCTL) medical student curriculum.
Both deliverables advance education at the intersection of generative AI, precision medicine, and bioethics. By raising awareness of patient innovation and emphasizing critical evaluation, privacy protection, and the value of professional healthcare guidance, this project supports responsible patient-initiated technology use.
Freedman Center Liaison: R. David Beales
Sensing the Body, Interpreting the Signal: An Open-Source Platform for Brain-Inspired Multimodal Health AI
Project Lead: M. Hassan Najafi
Affiliation(s)
- Associate Professor in the Electrical, Computer, and Systems Engineering
Project Description
This project develops an open-source AI platform for wearable health monitoring that is transparent, energy-efficient, and privacy-preserving. Most AI systems in commercial wearables are proprietary and opaque: researchers cannot reproduce them, educators cannot teach with them, and users cannot understand or contest how decisions about their own health are made. They also rely on deep learning methods that are power-hungry and cloud-dependent, which conflicts with the tight energy and privacy demands of always-on, on-device sensing. The platform unites two brain-inspired technologies into a single system that runs entirely on the device. It uses no backpropagation, no floating-point arithmetic, and no cloud connection, so sensitive health data never leaves the user's body. Each classification decision can be traced through interpretable symbolic operations, and a built-in unlearning mechanism lets users verifiably remove their data from the model. The platform follows open science principles throughout: open code, open data, open methods, and openly interrogable AI decisions. It will also serve as a teaching resource, with modules on AI transparency and health data ethics integrated into a graduate course on Next Generation Computing. All code, datasets, and materials will be released under open licenses.
Freedman Center Liaison: Jared Bendis
Seeing Structural Stigma: An Open-Source and AI-Assisted Mapping of Policy and Cultural Climates Toward Sexual Minority Populations Across U.S. States, 2006-2025
Project Lead: Haoming Song
Affiliation(s)
- Assistant Professor of Sociology
Project Description
Sexual minority populations, including lesbian, gay, bisexual, and other sexually diverse individuals, continue to experience persistent health disparities relative to their heterosexual counterparts. Recent scholarship has identified structural stigma as a key mechanism underlying these disparities, highlighting the role of societal conditions, cultural norms, and institutional policies. Despite substantial changes in legal protection and public attitudes toward sexual minority populations in the United States, longitudinal data capturing structural stigma over extended periods remain limited.
Building on my prior work across shorter time horizons (Song 2025; 2026), the project will develop an open-source, AI-empowered longitudinal dataset of state-level structural stigma toward sexual minority populations in the U.S. from 2006 to 2025. The project will construct a harmonized dataset that integrates policy and cultural indicators across time and place, providing comprehensive resources for tracking structural stigma across U.S. states. To maximize public impact, the project will also develop a public-facing website featuring interactive state-by-state maps and data visualization tools, benefiting researchers, educators, students (including research assistants), policymakers, journalists, advocates, and the public.
Overall, the (digital) project underscores both the importance of rigorous social science evidence and the transformative potential of public accessibility and transparency in advancing population health and social equity.
Freedman Center Liaison: R. Benjamin Gorham