Clinical Summaries of Social Media Timelines for Mental Health Monitoring: Human Versus Large Language Model Comparative Evaluation Study
At this time medium-size LLMs can generate largely accurate and informative clinical summaries of social media timelines, and advanced prompting boosts performance modestly. However, at the time of this writing, they underperform human clinicians in capturing subtle psychological nuances and individual idiosyncrasies. Future work should integrate domain‑specific fine‑tuning and enhanced context modeling to improve LLM clinical fidelity.
Authors
- Ayal Klein
- Jiayu Song (ORCID: https://orcid.org/0000-0003-4741-3876)
- Liran Keren
- Dana Atzil-Slonim
- Jenny Chim
- Andreas Triantafyllopoulos
- Björn W. Schuller
- Maria Liakata
Institutions
- Bar-Ilan University (IL)
- Queen Mary University of London (GB)
- The Alan Turing Institute (GB)
- Imperial College London (GB)
- Ariel University (IL)
- Technical University of Munich (DE)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2026-03-27
- DOI
- https://doi.org/10.2196/71230
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 0.00