ArticleNPJ digital medicine2025
Using a fine-tuned large language model for symptom-based depression evaluation.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- A Cautious Integration With AI in the Clinic: A Standardized-Patient Pilot Study of ChatGPT's Reliability in Hamilton Depression Rating Scale Scoring.Alpha psychiatry · 2026Article
- Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality.Brain sciences · 2026Review
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Observational
- Large language models to infer depression in patients with neurological conditions.BMJ neurology open · 2026Article
- From observation to evidence: harnessing the mental status examination as a real-world data source in clinical research.Frontiers in health services · 2026Article
- Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders.Frontiers in psychiatry · 2026Article
- Language-Based Detection of Depression with Machine Learning: Systematic Review and Meta- Analysis.Research square · 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
12 authors.
Funding
Abstract
Recent advances in artificial intelligence, particularly large language models (LLMs), show promise for mental health applications, including the automated detection of depressive symptoms from natural language. We fine-tuned a German BERT-based LLM to predict individual Montgomery-Åsberg Depression Rating Scale (MADRS) scores using a regression approach across different symptom items (0-6 severity scale), based on structured clinical interviews with transdiagnostic patients as well as synthetically generated interviews. The fine-tuned model achieved a mean absolute error of 0.7-1.0 across items, with accuracies ranging from 79 to 88%, closely matching clinician ratings. Fine-tuning resulted in a 75% reduction in prediction errors relative to the untrained model. These findings demonstrate the potential of lightweight LLMs to accurately assess depressive symptom severity, offering a scalable tool for clinical decision-making, and monitoring treatment progress, particularly in low-resource settings.
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.