# Let's Make It Personal, A Challenge in Personalizing Medical Inter-Human Communication

## TL;DR

This paper discusses the challenge of developing AI systems that assist medical professionals in personalizing communication with patients, emphasizing the importance of socially-relevant information for effective and trust-building interactions.

## Contribution

It introduces a four-part conceptual framework for personalized social interactions in medical communication and reviews current AI techniques and future challenges.

## Key findings

- Highlighting the gap in AI for social aspects of medical communication
- Proposing a framework for personalized human-to-human dialogue
- Discussing current AI techniques and future research directions

## Abstract

Current AI approaches have frequently been used to help personalize many aspects of medical experiences and tailor them to a specific individuals' needs. However, while such systems consider medically-relevant information, they ignore socially-relevant information about how this diagnosis should be communicated and discussed with the patient. The lack of this capability may lead to mis-communication, resulting in serious implications, such as patients opting out of the best treatment. Consider a case in which the same treatment is proposed to two different individuals. The manner in which this treatment is mediated to each should be different, depending on the individual patient's history, knowledge, and mental state. While it is clear that this communication should be conveyed via a human medical expert and not a software-based system, humans are not always capable of considering all of the relevant aspects and traversing all available information. We pose the challenge of creating Intelligent Agents (IAs) to assist medical service providers (MSPs) and consumers in establishing a more personalized human-to-human dialogue. Personalizing conversations will enable patients and MSPs to reach a solution that is best for their particular situation, such that a relation of trust can be built and commitment to the outcome of the interaction is assured. We propose a four-part conceptual framework for personalized social interactions, expand on which techniques are available within current AI research and discuss what has yet to be achieved.