ReviewFrontiers in psychiatry2021
Natural Language Processing as an Emerging Tool to Detect Late-Life Depression.
Review in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Speech and Language Markers of Bipolar Disorder: Challenges and Opportunities.Bipolar disorders · 2026Review
- Use and Performance of Language-based Artificial Intelligence (AI) Models to Screen for Depressive Disorders.Current psychiatry reports · 2026Review
- AI in geriatric psychiatry: precision meets human experience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Digital Therapeutics and Blended Care in Major Depressive Disorder: Current Evidence and Future Directions.Advances in experimental medicine and biology · 2026Review
- From fragmentation to integration: can social prescribing bridge the gap between multimorbidity and mental health in an ageing population?BMJ public health · 2026Review
- SPEAK-SAFE: secure processing of electronic audio for knowledge in suicide assessment from therapeutic exchanges.Frontiers in digital health · 2026Article
- Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.Cognitive therapy and research · 2025Article
- A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.Npj mental health research · 2025Article
- Artificial intelligence integration and human interaction in detecting depression in tuberculosis patients.Korean journal of family medicine · 2025Article
- Patients prefer human psychiatrists over chatbots: a cross-sectional study.Croatian medical journal · 2025Article
- Detecting ADHD through natural language processing and stylometric analysis of adolescent narratives.Frontiers in child and adolescent psychiatry · 2025Article
- Artificial intelligence implementation in the management of patients with tuberculosis.Korean journal of family medicine · 2025Article
- Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results.Journal of clinical medicine · 2024Article
- The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis.JMIR mental health · 2024Article
- Deep Learning and Geriatric Mental Health.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2024Review
- Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.Risk management and healthcare policy · 2024Review
- Artificial Intelligence: Deciphering the Links between Psychiatric Disorders and Neurodegenerative Disease.Brain sciences · 2023Article
- Natural Language Processing: from Bedside to Everywhere.Yearbook of medical informatics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.
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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.