Layperson-Friendly AI Translation of Medical Documents to Improve Doctor-Patient Communication: Protocols for the AI-INFOCARE and AI-MEDTALK Randomized Controlled Trials.

ABSTRACT:

Background

Many patients struggle to understand referral letters and discharge summaries; low health literacy is prevalent, and short consultations limit explanations. Large language models (LLMs) can translate clinical jargon into layperson language, but their impact on doctor-patient communication in real care remains untested.

Objective

This study aims to determine whether providing an artificial intelligence (AI)-generated, medically validated layperson-language translation of key medical documents before a consultation improves the quality of doctor-patient communication.

Methods

We plan to conduct two single-center, parallel-group randomized controlled trials in neurosurgery: AI-INFOCARE (outpatients prior to treatment discussions) and AI-MEDTALK (inpatients at

SUBMITTER: Hasani E 

PROVIDER: S-EPMC12680933 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature