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

arXiv 2507.21882 ↗First posted 29 Jul 2025cs.AI cs.SEcited by 0

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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.

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References · 24

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  3. 2023Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Reviewcited by 25
  4. 2024ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Modelscited by 15
  5. 2020Documentation of Machine Learning Softwarecited by 8
  6. 2019Asymmetric Release Planning-Compromising Satisfaction against Dissatisfactioncited by 4
  7. 2023Developers' Perception of GitHub Actions: A Survey Analysiscited by 4
  8. 2020Eye of the Mind: Image Processing for Social Codingcited by 3
  9. 2024T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviewscited by 2
  10. 2024GitHub Marketplace for Automation and Innovation in Software Productioncited by 2
  11. 2018A Longitudinal Study of Identifying and Paying Down Architectural Debtcited by 1
  12. 2019Mining Treatment-Outcome Constructs from Sequential Software Engineering Datacited by 1
12 more
  1. 2019Release Practices for Mobile Apps--What do Users and Developers Think?cited by 1
  2. 2023User Driven Functionality Deletion for Mobile Appscited by 1
  3. 2023Ownership in the Hands of Accountability at Brightsquid -- A Case Study and a Developer Surveycited by 1
  4. 2024Examining Ownership Models in Software Teams: A Systematic Literature Review and a Replication Studycited by 1
  5. 2024More Insight from Being More Focused: Analysis of Clustered Market Appscited by 1
  6. 2024Analysis of Marketed versus Not-marketed Mobile App Releasescited by 1
  7. 2024Negative Results of Image Processing for Identifying Duplicate Questions on Stack Overflowcited by 1
  8. 2024From Specifications to Prompts: On the Future of Generative LLMs in Requirements Engineeringcited by 1
  9. 2024Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Appscited by 1
  10. 2025Inferring Questions from Programming Screenshotscited by 1
  11. 2025ImageR: Enhancing Bug Report Clarity by Screenshotscited by 1
  12. 2025One Documentation Does Not Fit All: Case Study of TensorFlow Documentationcited by 1

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