Harnessing AI and Machine Learning for Precision Medicine: Advancements in Genomic Research, Disease Detection, and Personalized Healthcare
Year: 2025
Type: article
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) into precision medicine is driving transformative advancements in genomic research, disease detection, and personalized healthcare. AI and ML algorithms are enhancing the ability to analyze complex genomic data, enabling the identification of novel biomarkers and the understanding of gene-environment interactions at an unprecedented scale. In genomic research, these technologies are facilitating the discovery of genetic variants associated with diseases, offering new insights into disease mechanisms and potential therapeutic targets. In disease detection, AI-driven models, such as deep learning and natural language processing, are improving diagnostic accuracy, particularly in imaging, genomics, and clinical data analysis, enabling early detection of conditions such as cancer, neurodegenerative diseases, and cardiovascular disorders. Personalized healthcare is also benefiting from AI and ML, as algorithms optimize treatment regimens, predict patient responses to therapies, and provide decision support tools tailored to individual genetic profiles. Reinforcement learning and survival analysis models are being utilized to personalize patient care, ensuring that treatments are both effective and efficient. This abstract discusses the state-of-the-art advancements in AI and ML for precision medicine, highlighting key challenges, opportunities, and the potential for these technologies to revolutionize healthcare delivery through more accurate, personalized, and proactive approaches.
Author: Mahesh Recharla
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4409295934