Rugvid Parmar1, Bhupendra Parmar2, Tesfaye Rebuma3, Mahendra Pal4,*
1B. J. Medical College, Civil Hospital Campus, Ahmedabad 380016, Gujarat, India.
2Department of Veterinary Public Health, College of Veterinary Science, Anand 388001, Gujarat, India.
3Sebeta Sub-City Agricultural Office, Shaggar City Administration, Sebeta 2355, Oromia, Ethiopia.
4Narayan Consultancy on Veterinary Public Health and Microbiology, Saphire Lifestyle, Bharuch 392012, Gujarat, India.
*Corresponding author: Mahendra Pal
Abstract
Integrating artificial intelligence (AI) with multi-modal engineering systems is poised to revolutionize personalized medicine, an innovative approach that tailors healthcare interventions based on the unique physiological, genetic, and lifestyle characteristics of each patient. This paper explores how AI enables the comprehensive analysis of vast and complex datasets sourced from wearable sensors, advanced imaging technologies, genomic sequencing, and electronic health records. By synthesizing diverse types of information, AI enhances diagnostic accuracy, facilitates early disease detection, and supports the development of highly individualized treatment plans, thereby significantly improving therapeutic outcomes for patients. Despite these promising advancements, the implementation of AI in personalized medicine faces several critical challenges, including the complexity of integrating heterogeneous data, ensuring robust patient privacy and data security, and improving the interpretability of AI-driven models for clinical decision-making. Addressing these challenges requires strong interdisciplinary collaboration among clinicians, data scientists, engineers, and ethicists. Through these concerted efforts, AI can be effectively harnessed to deliver high-performance, ethical, and patient-centered healthcare solutions.
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