The Impact of Foundational Models on Patient-Centric e-Health Systems

TL;DR

This paper assesses the current state of AI integration in patient-centric healthcare apps, revealing most are in early development stages and highlighting the need for further AI maturity to improve trust and transparency.

Contribution

It introduces a method to evaluate AI maturity in healthcare applications using LLMs and categorizes their integration stages based on the Gartner AI model.

Findings

86.21% of apps are at early AI integration stages

13.79% of apps demonstrate advanced AI integration

Highlights the gap in AI maturity in healthcare apps

Abstract

As Artificial Intelligence (AI) becomes increasingly embedded in healthcare technologies, understanding the maturity of AI in patient-centric applications is critical for evaluating its trustworthiness, transparency, and real-world impact. In this study, we investigate the integration and maturity of AI feature integration in 116 patient-centric healthcare applications. Using Large Language Models (LLMs), we extracted key functional features, which are then categorized into different stages of the Gartner AI maturity model. Our results show that over 86.21\% of applications remain at the early stages of AI integration, while only 13.79% demonstrate advanced AI integration.