The Impact of Foundational Models on Patient-Centric e-Health Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911081782312960 |
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| author | Onagh, Elmira Davoodi, Alireza Nayebi, Maleknaz |
| author_facet | Onagh, Elmira Davoodi, Alireza Nayebi, Maleknaz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21882 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Impact of Foundational Models on Patient-Centric e-Health Systems Onagh, Elmira Davoodi, Alireza Nayebi, Maleknaz Artificial Intelligence Software Engineering 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. |
| title | The Impact of Foundational Models on Patient-Centric e-Health Systems |
| topic | Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2507.21882 |