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

2025-07-29

AI Summary

This study assesses the practical maturity of artificial intelligence (AI) in patient-centered electronic health (e-Health) systems, focusing on trustworthiness, transparency, and real-world deployment efficacy. Method: Leveraging the Gartner AI Maturity Model, we conduct the first systematic, large-scale evaluation of 116 e-Health systems by integrating large language models (LLMs) for automated functional feature extraction and stage-based classification—thereby coupling LLM-driven semantic parsing with a canonical AI maturity framework to enable scalable, reproducible maturity diagnostics. Contribution/Results: Findings reveal that 86.21% of systems remain at the early integration stage, while only 13.79% achieve advanced integration—indicating severe underutilization of AI’s transformative potential in e-Health. This work establishes an empirical benchmark and methodological paradigm to inform policy formulation, system design, and technology roadmapping in AI-augmented healthcare.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Assess AI maturity in patient-centric healthcare applications
• Evaluate trustworthiness and transparency of AI in e-health
• Analyze integration stages of AI features using LLMs

Innovation

• Using Large Language Models for feature extraction
• Categorizing features via Gartner AI maturity model
• Evaluating AI integration in 116 healthcare apps