Interpreting psychiatric digital phenotyping data with large language models: a preliminary analysis.

ABSTRACT:

Background

Digital phenotyping provides passive monitoring of behavioural health but faces implementation challenges in translating complex multimodal data into actionable clinical insights. Digital navigators, healthcare staff who interpret patient data and relay findings to clinicians, provide a solution, but workforce limitations restrict scalability.

Objective

This study provides one of the first systematic evaluation of large language model performance in interpreting simulated psychiatric digital phenotyping data, establishing baseline accuracy metrics for this emerging application.

Methods

We evaluated GPT-4o and GPT-3.5-turbo across over 153 test cases covering various clinical scenarios, timeframes and data quality levels using simulated test datasets current

SUBMITTER: Flathers M 

PROVIDER: S-EPMC12458714 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature