Published Paper Details:

The key objective of this study is to fill the gap between personalized medicine and healthcare that currently exists by integrating forecasting diagnosis channels and individualized treatment made possible by artificial intelligence (AI). With advancements in big data analytics, machine learning, and deep neural networks, this research explores how AI can revolutionize healthcare by facilitating exact predictions of disease risk, diagnosis, and treatment response tailored to each patient. For instance, AI includes implementing deep neural networks for dermatologist-level classification of skin cancer and AI usage in cardiology for risk prediction and diagnosis [1]. This study outlines the existing literature and recently challenging issues in technology to explain the opportunities that AI-based personalized medicine will offer people to improve the outcome of their health, reduce the wastage of resources, and, lastly, minimize health inequalities. Secondly, this paper looks at the implications of personalized medicine approaches on the US healthcare system regarding managing chronic conditions like diabetes [1]. Today, personalized treatment plans based on the patient profile are preferred over one medication regime for all patients. Similarly, it also underscores the potential for better productivity, cost reduction, and patient-oriented treatment. Ultimately, it serves to outline the future of personalized medicine in the US with particular emphasis on two main topics, which are the persisting challenges and the opportunities for further development and adoption of such technology, such as integration of genomic data into clinical decision making to fit treatment of cancer patients.