Machine Learning–Based Audiovisual Phenotyping for Measuring Communication, Shared Decision-Making, and Trust
Machine learning-based audiovisual phenotyping can reveal hidden discrepancies between patients' self-reported experiences and nonverbal expressions, offering a promising tool for objectively assessing communication quality and advancing health equity.
Authors
- Alice Baird (ORCID: https://orcid.org/0000-0002-7003-5650)
- Vineet Tiruvadi (ORCID: https://orcid.org/0000-0001-5071-7091)
- Jaime R. Fineman
- Michael L. Birnbaum (ORCID: https://orcid.org/0000-0002-4285-7868)
- Laura Igarabuza (ORCID: https://orcid.org/0000-0003-0912-4889)
- Anne-Catherine Grela-Mpoko
- Jadyn Crossley
- Lindsey Hoffman
- Shely Khaikin
- Menachem Leasy
- Margot Savoy
- Jeffrey Brooks
- Anuradha Paranjape
Institutions
- American Academy of Family Physicians (US)
- Harvard University Press (US)
- New York Psychoanalytic Society and Institute (US)
- Massachusetts General Hospital (US)
- New York State Psychiatric Institute
- University College London (GB)
- Columbia University (US)
- Temple University (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2026-01-28
- DOI
- https://doi.org/10.2196/85906
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Temple University