ReviewAnnual review of clinical psychology2026
Computational Analysis of Expressive Behavior in Clinical Assessment.
Review in Annual review of clinical psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Clinical psychological assessment often relies on self-report, interviews, and behavioral observation, methods that pose challenges for reliability, validity, and scalability. Computational approaches offer new opportunities to analyze expressive behavior (e.g., facial expressions, vocal prosody, language use) with greater precision and efficiency. This review provides an accessible conceptual framework for understanding how methods from computer vision, speech signal processing, and natural language processing can enhance clinical assessment. We outline the goals, frameworks, and methods of both clinical and computational approaches and present an illustrative review of interdisciplinary research applying these techniques across a range of mental health conditions. We also examine key challenges related to data quality, measurement, interdisciplinarity, and ethics. Finally, we highlight future directions for building systems that are robust, interpretable, and clinically meaningful. This review is intended to support dialogue between clinical and computational communities and to guide ongoing research and development at their intersection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.