Evidence map›Paper›PMID 41823725›Full record

ArticleJMIR formative research2026

Machine Learning-Based Audiovisual Phenotyping for Measuring Communication, Shared Decision-Making, and Trust.

Shely Khaikin, Vineet Tiruvadi, Jeffrey Brooks, Alice Baird, Anne-Catherine Grela-Mpoko, Lindsey Hoffman, Jadyn Crossley, Menachem Leasy, Jaime Fineman, Margot Savoy and 5 more

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shely KhaikinShared Decision Making Laboratory, Temple University, Philadelphia, PA, United States.ORCID 0009-0002-2803-9735
Vineet TiruvadiHarvard University, Cambridge, MA, United States.ORCID 0000-0001-5071-7091
Jeffrey BrooksHume AI, New York, NY, United States.ORCID 0000-0002-6370-3622
Alice BairdHume AI, New York, NY, United States.ORCID 0000-0002-7003-5650
Anne-Catherine Grela-MpokoShared Decision Making Laboratory, Temple University, Philadelphia, PA, United States.ORCID 0009-0009-1597-1911
Lindsey HoffmanShared Decision Making Laboratory, Temple University, Philadelphia, PA, United States.ORCID 0009-0003-0330-7024
Jadyn CrossleyShared Decision Making Laboratory, Temple University, Philadelphia, PA, United States.ORCID 0009-0004-7549-6300
Menachem LeasyDepartment of Clinical Family and Community Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States.ORCID 0009-0003-2597-4530
Jaime FinemanDepartment of Clinical Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States.ORCID 0009-0005-2437-1417
Margot SavoyAmerican Academy of Family Physicians, Washington, DC, United States.ORCID 0000-0002-2911-8059
Laura IgarabuzaDepartment of Clinical Family and Community Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States.ORCID 0000-0003-0912-4889
Anuradha ParanjapeUniversity of Colorado School of Medicine, Aurora, CO, United States.ORCID 0000-0003-3151-9932
Cheryl Ys FooCenter of Excellence for Psychosocial and Systemic Research, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States.ORCID 0000-0002-0687-9840
Michael L BirnbaumNew York State Psychiatric Institute, New York, NY, United States.ORCID 0000-0002-4285-7868
Yaara Zisman-IlaniDepartment of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, 1-19 Torrington Place, London, WC1E 7Hb, United Kingdom.ORCID 0000-0001-6852-2583

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: 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.

Indexed as

CommunicationDecision Making, SharedMachine LearningTrustHumansPhenotypeAIartificial intelligenceaudiovisual digital phenotypingdepressionnatural language processingprimary careshared decision-making

Identifiers

PMID41823725
PMCPMC12984015

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.