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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15334157 |
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| author | Researchscholar |
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| contents | <p>Medication adherence is a critical component of successful healthcare management, especially for patients with chronic conditions. However, non-adherence to prescribed medication regimens is a widespread issue, leading to poor health outcomes, increased hospitalizations, and higher healthcare costs. Traditional methods for monitoring and improving adherence have limitations, prompting the exploration of AI-driven solutions. This paper examines the role of AI-augmented systems in enhancing medication adherence through personalized support, real-time monitoring, and predictive analytics. We explore AI techniques such as machine learning, natural language processing, and predictive modeling to address the complex challenges associated with medication adherence. The paper highlights AI-powered mobile applications, virtual assistants, and wearable devices that can offer tailored adherence plans and behavioral interventions. Case studies and clinical implementations demonstrate the effectiveness of AI systems in improving adherence in patients with chronic diseases. Ethical, legal, and practical considerations in AI adoption for healthcare are discussed, with a focus on patient data privacy and integration challenges. The paper concludes by exploring future directions for AI in medication adherence, including personalized health systems and potential applications in global health. By leveraging AI, healthcare systems can overcome barriers to adherence, improve patient outcomes, and reduce the overall burden on the healthcare system.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15334157 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | AI-Augmented Systems for Medication Adherence Researchscholar <p>Medication adherence is a critical component of successful healthcare management, especially for patients with chronic conditions. However, non-adherence to prescribed medication regimens is a widespread issue, leading to poor health outcomes, increased hospitalizations, and higher healthcare costs. Traditional methods for monitoring and improving adherence have limitations, prompting the exploration of AI-driven solutions. This paper examines the role of AI-augmented systems in enhancing medication adherence through personalized support, real-time monitoring, and predictive analytics. We explore AI techniques such as machine learning, natural language processing, and predictive modeling to address the complex challenges associated with medication adherence. The paper highlights AI-powered mobile applications, virtual assistants, and wearable devices that can offer tailored adherence plans and behavioral interventions. Case studies and clinical implementations demonstrate the effectiveness of AI systems in improving adherence in patients with chronic diseases. Ethical, legal, and practical considerations in AI adoption for healthcare are discussed, with a focus on patient data privacy and integration challenges. The paper concludes by exploring future directions for AI in medication adherence, including personalized health systems and potential applications in global health. By leveraging AI, healthcare systems can overcome barriers to adherence, improve patient outcomes, and reduce the overall burden on the healthcare system.</p> |
| title | AI-Augmented Systems for Medication Adherence |
| url | https://doi.org/10.5281/zenodo.15334157 |