ReviewInteractive journal of medical research2024
Screening for Depression Using Natural Language Processing: Literature Review.
Review in Interactive journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled 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.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Responsible and innovative AI for mental health care: five priority themes.NPP - digital psychiatry and neuroscience · 2026Review
- Benchmarking large language models against practicing clinicians on psychopathological assessment.NPJ digital medicine · 2026Article
- Alignment of Large Language Model Responses With Human Therapists in Motivational Interviewing.JAMA network open · 2026Article
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol.JMIR research protocols · 2026Article
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- From fragmentation to integration: can social prescribing bridge the gap between multimorbidity and mental health in an ageing population?BMJ public health · 2026Review
- Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language.Frontiers in psychiatry · 2026Article
- A Novel Integrative Framework for Depression: Combining Network Pharmacology, Artificial Intelligence, and Multi-Omics with a Focus on the Microbiota-Gut-Brain Axis.Current issues in molecular biology · 2025Review
- Language-Based Detection of Depression with Machine Learning: Systematic Review and Meta- Analysis.Research square · 2025Article
- AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions.JMIR mental health · 2025Article
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- Multilingual sentiment analysis in restaurant reviews using aspect focused learning.Scientific reports · 2025Article
- A Scoping Review of Arabic Natural Language Processing for Mental Health.Healthcare (Basel, Switzerland) · 2025Review
- MOODMIND: A Pilot Feasibility Study of Artificial Intelligence for Major Depressive Disorder Screening in Tuberculosis Patients.F1000Research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations.
objectiveThis literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases.
methodsA literature search was conducted using Semantic Scholar, PubMed, and Google Scholar to identify studies on depression screening using NLP. Keywords included "depression screening," "depression detection," and "natural language processing." Studies were included if they discussed the application of NLP techniques for depression screening or detection. Studies were screened and selected for relevance, with data extracted and synthesized to identify common themes and gaps in the literature.
resultsNLP techniques, including sentiment analysis, linguistic markers, and deep learning models, offer practical tools for depression screening. Supervised and unsupervised machine learning models and large language models like transformers have demonstrated high accuracy in a variety of application domains. However, ethical concerns related to privacy, bias, interpretability, and lack of regulations to protect individuals arise. Furthermore, cultural and multilingual perspectives highlight the need for culturally sensitive models.
conclusionsNLP presents opportunities to enhance depression detection, but considerable challenges persist. Ethical concerns must be addressed, governance guidance is needed to mitigate risks, and cross-cultural perspectives must be integrated. Future directions include improving interpretability, personalization, and increased collaboration with domain experts, such as data scientists and machine learning engineers. NLP's potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation.
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.