Convergence of AI and Healthcare: A Review of Machine Learning Applications

Abstract

The integration of AI and healthcare can act as the foundation for improving the medical business and the diagnosis and treatment process, as well as patient throughputs. Guided by the following objectives: the current review seeks to discuss what can perhaps be considered as the most revolutionary aspect of care: namely, use of the ML. Software such as the radiology algorithms as well as the prediction tools in healthcare are already narrowing down the error margin. This shifting of life through the practice of genetics by the improvement of AI in managing genetics as the world turns to individualized approach of patients. However, there is a list of challenges that arise when applying AI in the healthcare industry: Data protection and Algorithmic or and The question of who or what is responsible for the AI applications. Clinical decision involving the use of AI has ethical and regulatory issues that need to be addressed therein. But AI does hold a massive amount in advancing the clinical results, in reducing the costs and in making the health care system much stronger, proactive, personalized and efficient. As to the future trends for the use of AI in health care; it will be employed in pharmacology and drug development; in surgery through robot control; and patient management through tele monitoring; as well as in precision care and health information analytics and forecasting. New solutions to still pending issues in data protection, data sharing and objectivity will be important in the future of AI in health care. In sum, this paper proposed that AI is an innovative tool in healthcare’s, that has the potential to redefine the possibilities of how patient care can be delivered, and clinical work can be done, provided the steering wheel of ethical and regulatory burdens is pulled well.