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

arXiv: https://arxiv.org/abs/2507.21882 · HTML: https://mlsanity.com/paper/2507.21882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions](https://mlsanity.com/paper/2404.03264/) (2024)
- [Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review](https://mlsanity.com/paper/2306.12834/) (2023)
- [ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models](https://mlsanity.com/paper/2405.18638/) (2024)
- [Documentation of Machine Learning Software](https://mlsanity.com/paper/2001.11956/) (2020)
- [Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction](https://mlsanity.com/paper/1901.05130/) (2019)
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- [Eye of the Mind: Image Processing for Social Coding](https://mlsanity.com/paper/2001.06426/) (2020)
- [T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviews](https://mlsanity.com/paper/2401.03833/) (2024)
- [GitHub Marketplace for Automation and Innovation in Software Production](https://mlsanity.com/paper/2407.05519/) (2024)
- [A Longitudinal Study of Identifying and Paying Down Architectural Debt](https://mlsanity.com/paper/1811.12904/) (2018)
- [Mining Treatment-Outcome Constructs from Sequential Software Engineering Data](https://mlsanity.com/paper/1901.05604/) (2019)
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- [Analysis of Marketed versus Not-marketed Mobile App Releases](https://mlsanity.com/paper/2405.15752/) (2024)
- [Negative Results of Image Processing for Identifying Duplicate Questions on Stack Overflow](https://mlsanity.com/paper/2407.05523/) (2024)
- [From Specifications to Prompts: On the Future of Generative LLMs in Requirements Engineering](https://mlsanity.com/paper/2408.09127/) (2024)
- [Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Apps](https://mlsanity.com/paper/2410.07370/) (2024)
- [Inferring Questions from Programming Screenshots](https://mlsanity.com/paper/2504.18912/) (2025)
- [ImageR: Enhancing Bug Report Clarity by Screenshots](https://mlsanity.com/paper/2505.01925/) (2025)
- [One Documentation Does Not Fit All: Case Study of TensorFlow Documentation](https://mlsanity.com/paper/2505.01939/) (2025)

## Abstract (arXiv)

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.