ArticleJMIR mental health2021
Language, Speech, and Facial Expression Features for Artificial Intelligence-Based Detection of Cancer Survivors' Depression: Scoping Meta-Review.
Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- The Preliminary Efficacy and Feasibility of SMILE Serious Game for Adolescents and Young Adults: Protocol for Cluster Randomized Multisite Adaptive Trial Design.JMIR research protocols · 2026Article
- Use and Performance of Language-based Artificial Intelligence (AI) Models to Screen for Depressive Disorders.Current psychiatry reports · 2026Review
- Cancer Patients' Perception, Acceptance, and Utilization of Artificial Intelligence-Based Emotional Distress Assessment Tools: A Scoping Review.Cancer medicine · 2026Article
- Pain Cues in People With Dementia: Scoping Review.JMIR mental health · 2025Article
- Research progress on the psychological burden and intervention measures in cancer patients.Frontiers in psychiatry · 2024Review
- Improving Psychiatry Services with Artificial Intelligence: Opportunities and Challenges.Turk psikiyatri dergisi = Turkish journal of psychiatry · 2024Review
- A review on the efficacy of artificial intelligence for managing anxiety disorders.Frontiers in artificial intelligence · 2024Review
- Intelligent oncology: The convergence of artificial intelligence and oncology.Journal of the National Cancer Center · 2023Review
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models.Diagnostics (Basel, Switzerland) · 2022Article
- Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians.Frontiers in psychiatry · 2022Article
- Decoding anxiety: A scoping review of observable cues.Digital healthArticle
- Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy.Digital healthReview
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states.
objectiveThe aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors.
methodsWe followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers.
resultsThe search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression.
conclusionsThis review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence-supported screening for depression in cancer survivors.
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Registered trials
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