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Research Paper | Information Security | United States of America | Volume 13 Issue 7, July 2024 | Popularity: 4.8 / 10

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Utilizing Artificial Intelligence for Patient Risk Predictions: Empowering Doctors with Data - Driven Insights

Sivachandran Selvaraj

Abstract: The integration of artificial intelligence (AI) in healthcare is revolutionizing precision medicine, enabling advanced patient risk prediction and providing healthcare professionals with data - driven insights. This research explores AI's transformative potential, focusing on empowering doctors to make informed decisions based on accurately predicted patient risks. Leveraging AI algorithms for real - time data analysis allows healthcare providers to tailor personalized treatment plans, optimize care, and enhance outcomes. AI - driven risk prediction equips doctors with a proactive approach to address potential health issues before they escalate, leading to improved patient outcomes and more efficient healthcare delivery. By identifying individuals at higher risk for specific conditions, interventions can be targeted and tailored, potentially preventing complications and reducing healthcare costs. This study highlights the pivotal role of AI in augmenting medical decision - making processes. AI models can analyze vast amounts of patient data, including medical histories, genetic information, lifestyle factors, and real - time physiological data, to generate comprehensive risk profiles. This information empowers healthcare professionals to make more informed diagnoses, select appropriate treatments, and monitor patient progress more effectively. Ultimately, the integration of AI in healthcare has the potential to revolutionize how data - driven insights are harnessed to enhance patient care strategies and optimize healthcare systems.

Keywords: Artificial intelligence (AI), Healthcare AI, Patient risk prediction, Risk assessment, Predictive analytics, Data - driven insights, Clinical decision support, US Healthcare, AI algorithms for healthcare

Edition: Volume 13 Issue 7, July 2024

Pages: 371 - 376

DOI: https://www.doi.org/10.21275/SR24707023233

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