AI-Enhanced Personalized Medicine: Machine Learning Approaches for Tailoring Treatment Plans Based on Genetic, Clinical, and Lifestyle Data
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Keywords

Personalized Medicine
Treatment Plans

How to Cite

[1]
Dr. Hans Müller, “AI-Enhanced Personalized Medicine: Machine Learning Approaches for Tailoring Treatment Plans Based on Genetic, Clinical, and Lifestyle Data”, Journal of Bioinformatics and Artificial Intelligence, vol. 3, no. 2, pp. 237–252, Aug. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/105

Abstract

The treatment of patients historically relies on the use of medicines that work best for the larger population but could have variable effects or adverse reactions in some individuals. The release of such drugs has justified the use of a one-size-fits-all approach, where the prescription decision is not individualized based on patient characteristics. A paradigm shift in healthcare is observed when the most effective therapeutic strategies for specific patient populations are designed. The convergence of genetic variation, clinical features, and lifestyle factors provides the biggest promise for increasing therapeutic efficacy and patient safety in recent years. This has encouraged the initiation and provision of data and clinical trial arms to treat the affected populations. The application of AI in analyzing these multilevel data to find the hidden patterns, novel connections, and actionable insights may aid in the prior and custom therapeutic strategy.

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