ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE: TECHNICAL, LEGAL, AND BUSINESS PERSPECTIVES ON THE FUTURE OF HEALTHCARE

Abstract

The advent of Artificial Intelligence (AI) has proved to be an underpinning tool of precision medicine. AI has the potential to become a key technology for disease severity prediction, diagnosis, and tailored treatment plans. Our research discusses AI as a useful technology for precision medicine from three viewpoints: technical, legal, and business, with a focus on genomic data, medical imaging, and electronic health clinical records. The entire study places emphasis on sources of data, with a discussion comparing deep learning and machine learning approaches in the aspect of accuracy. It should be noted that Random Forest models achieved optimal accuracy at 91.8%, Gradient Boosting Machines achieved 90.5%, and Convolutional Neural Networks (CNN) represented 94.2% accuracy when used in medical image classification. The support vector machine had informative findings at 88.7% accuracy in high-dimensional genomic analysis. Additionally, the research discussed some of the legal and ethical issues that have been concerned with the use of AI in precision medicine. The authors confirm there are considerations for appropriate data use regarding patients' designated rights to anonymity, data privacy, transparency, algorithmic fairness, and the accountability model for considering clinical results from AI systems. Simultaneously, there are different indicators that AI will yield appreciable cost savings and efficiency benefits for businesses within the precision medicine ecosystem. However, there are still obstacles to scalability, interoperability, and workforces to adjust for the business in order to thrive. In general, the research discovers that the efficacy at high levels and precise methods established to provide precision medicine indicates AI-guided precision medicine can learn from best practices by combining technical reinvention with ethics and long-term sustainability guarantees.