# Machine Learning–Based Audiovisual Phenotyping for Measuring Communication, Shared Decision-Making, and Trust

## TL;DR

This paper explores using machine learning to analyze audio and video data to better understand patient communication and trust in healthcare.

## Contribution

The novelty lies in using audiovisual phenotyping to objectively assess communication and decision-making in healthcare.

## Key findings

- Machine learning can detect discrepancies between self-reported experiences and nonverbal cues.
- This approach offers an objective way to evaluate communication quality in healthcare settings.
- It has potential to promote health equity by improving shared decision-making.

## Abstract

Machine learning–based audiovisual phenotyping can reveal hidden discrepancies between patients’ self-reported experiences and nonverbal expressions, offering a promising tool for objectively assessing communication quality and advancing health equity.