01 October 2026 | StageOne, Zurich
Making Long-Term Multimodal Information Useful with AI — A Therapy Case Study
Long-term therapeutic processes often involve large amounts of fragmented information distributed across months or even years of sessions. Therapists frequently work with handwritten or spoken notes, scans, PDFs, and recurring patient references that can become difficult to organize, retrieve, and connect over time.
In this workshop, we explore how AI systems can support therapists by improving the handling of long-term therapeutic information. We discuss approaches for transforming handwritten or spoken session notes into usable text, extracting information from PDFs and scans, structuring the memory of past sessions, and retrieving relevant information across therapy history.
On the use case of mental health therapy, we investigate how AI can support existing therapeutic practices and present early feedback collected from therapists. Finally, we discuss practical challenges, opportunities, and future directions for AI-assisted long-term information organization.
