Synthetic patient and interview transcript creator: an essential tool for LLMs in mental health.

ABSTRACT: Developing high-quality training data is essential for tailoring large language models (LLMs) to specialized applications like mental health. To address privacy and legal constraints associated with real patient data, we designed a synthetic patient and interview generation framework that can be tailored to regional patient demographics. This system employs two locally run instances of Llama 3.3:70B: one as the interviewer and the other as the patient. These models produce contextually rich interview transcripts, structured by a customizable question bank, with lexical diversity similar to normal human conversation. We calculate median Distinct-1 scores of 0.44 and 0.33 for the patient and interview assistant model outputs respectively compared to 0.50 ± 0.11 as the average for 10,000 epis

SUBMITTER: Warner A 

PROVIDER: S-EPMC12460306 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature