Designing and Implementing Human-Centered AI: Comparing and Contrasting Approaches for Clinician and Patient Users
Artificial Intelligence (AI) has the potential to improve patient care and reduce clinician workload—but its success depends on how well it fits the real-world needs and workflows of patients and clinicians. This panel brings together experts in the design and implementation of human-centered AI across diverse healthcare settings, including emergency departments, outpatient care, and home- and community-based care. Panelists will compare and contrast methods for designing and implementing AI across contexts, users, and technologies. They will consider critical challenges such as supporting user understanding, enabling appropriate use, avoiding inequities in AI access and outcomes, and ensuring AI tools align with both clinician and patient goals. Attendees will gain practical insights into how human-centered methods can be applied to design and implement AI systems that improve care delivery while preserving clinician agency and advancing equity.
Authors
- Natalie C. Benda (ORCID: https://orcid.org/0000-0002-3256-0243)
- Nicole E. Werner (ORCID: https://orcid.org/0000-0003-2363-9491)
- Enid N. H. Montague (ORCID: https://orcid.org/0009-0005-7387-4248)
- Megan Elizabeth Salwei (ORCID: https://orcid.org/0000-0002-5457-2501)
- Erkin Ötleş (ORCID: https://orcid.org/0000-0003-3169-6832)
- Hanna J. Barton (ORCID: https://orcid.org/0000-0003-0457-1153)
Institutions
- University of Wisconsin–Madison (US)
- University of Toronto (CA)
- Columbia University Irving Medical Center (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1177/10711813251369399
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00