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16 reviewsRecent advances in artificial intelligence, particularly large language models (LLMs), show promise formental health applications, including the automated detection of depressive symptoms from natural1234567890():,;1234567890():,;language. We fine-tuned a German BERT-based LLM to predict individual Montgomery-ÅsbergDepression Rating Scale (MADRS) scores using a regression approach across different symptomitems (0–6 severity scale), based on structured clinical interviews with transdiagnostic patients as wellas synthetically generated interviews. The fine-tuned model achieved a mean absolute error of 0.7–1.0across items, with accuracies ranging from 79 to 88%, closely matching clinician ratings. Fine-tuningresulted in a 75% reduction in prediction errors relative to the untrained model. These findingsdemonstrate the potential of lightweight LLMs to accurately assess depressive symptom severity,offering a scalable tool for clinical decision-making, and monitoring treatment progress, particularly inlow-resource settings.
