Large language models in psychiatry

• Verfasst von: Volkmer, Sebastian [VerfasserIn]
• Verfasst von: Meyer-Lindenberg, Andreas [VerfasserIn]
• Verfasst von: Schwarz, Emanuel [VerfasserIn]
• Titel: Large language models in psychiatry
• Titelzusatz: opportunities and challenges
• Verf.angabe: Sebastian Volkmer, Andreas Meyer-Lindenberg, Emanuel Schwarz
• E-Jahr: 2024
• Jahr: September 2024
• Umfang: 8 S.
• Illustrationen: Illustrationen
• Fussnoten: Online verfügbar: 11. Juni 2024, Artikelversion: 22. Juni 2024 ; Gesehen am 22.10.2024
• Titel Quelle: Enthalten in: Psychiatry research
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1979
• Jahr Quelle: 2024
• Band/Heft Quelle: 339(2024), Artikel-ID 116026$m9, Seite 1-8
• ISSN Quelle: 1872-7123
• DOI: doi:10.1016/j.psychres.2024.116026
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: BERT
• Sach-SW: GPT
• Sach-SW: Hallucination
• Sach-SW: Llama
• Sach-SW: Medical question answering
• Sach-SW: PaLM
• Sach-SW: Therapy
• Sach-SW: Transformer
• K10plus-PPN: 190637273X

Abstract

The ability of Large Language Models (LLMs) to analyze and respond to freely written text is causing increasing excitement in the field of psychiatry; the application of such models presents unique opportunities and challenges for psychiatric applications. This review article seeks to offer a comprehensive overview of LLMs in psychiatry, their model architecture, potential use cases, and clinical considerations. LLM frameworks such as ChatGPT/GPT-4 are trained on huge amounts of text data that are sometimes fine-tuned for specific tasks. This opens up a wide range of possible psychiatric applications, such as accurately predicting individual patient risk factors for specific disorders, engaging in therapeutic intervention, and analyzing therapeutic material, to name a few. However, adoption in the psychiatric setting presents many challenges, including inherent limitations and biases in LLMs, concerns about explainability and privacy, and the potential damage resulting from produced misinformation. This review covers potential opportunities and limitations and highlights potential considerations when these models are applied in a real-world psychiatric context.