AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems

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José Gabriel Carrasco Ramírez

Abstract

This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.

Article Details

How to Cite
Carrasco Ramírez, J. G. . (2024). AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 1(1), 31-37. https://doi.org/10.60087/jaigs.v1i1.p37
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Article

How to Cite

Carrasco Ramírez, J. G. . (2024). AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 1(1), 31-37. https://doi.org/10.60087/jaigs.v1i1.p37

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