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Analysis Study Research Paper | Computer Science and Information Technology | United States of America | Volume 13 Issue 7, July 2024 | Popularity: 4.8 / 10

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Empowering Patients with AI-Driven Personalized Care: The Transformative Power of Generative AI and Healthcare Data Integration

Sivachandran Selvaraj

Abstract: In an era of unprecedented technological advancement, generative artificial intelligence (AI) is poised to revolutionize healthcare, particularly in the realm of personalized patient care. This abstract explores how the integration of generative AI with comprehensive healthcare data can empower patients by tailoring treatments, enhancing decision-making, and improving overall health outcomes. By harnessing the power of generative AI, healthcare providers can analyze vast amounts of patient data, including medical histories, genetic information, lifestyle factors, and real-time health metrics. This holistic approach enables the generation of personalized treatment plans, risk assessments, and preventive interventions, ultimately leading to more effective and targeted care. Moreover, generative AI can facilitate seamless communication between patients and providers, enhancing patient engagement and understanding. Through natural language processing and conversational AI, patients can access personalized health information, receive tailored education, and engage in virtual consultations, fostering a patient-centric approach to healthcare. This abstract will delve into the transformative potential of generative AI in data integration, showcasing real-world applications and highlighting the ethical considerations surrounding its implementation. By embracing this technological paradigm shift, healthcare systems can empower patients, enhance the quality of care, and pave the way for a more personalized and patient-centered future of medicine.

Keywords: Generative AI, Data Integration, Healthcare, LLM, Deep Learning, AI in Healthcare, Patient Care, Language

Edition: Volume 13 Issue 7, July 2024

Pages: 337 - 343

DOI: https://www.doi.org/10.21275/SR24703063340

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