AI in Clinical Practice: Current Uses and the Path Forward

Abstract

Machine learning is developing very quickly in the healthcare field in the field’s diagnostics, therapy and caring of patients and new drug development. Machine learning, deep learning, and so on have opened up new areas of diagnosis of diseases, understanding what particular treatment is required for a specific patient, and prognosis of disease. Radiology, Computer Aided Diagnosis and chronic disease applications are the current areas covered by AI to improve the health of patients and to hasten the speed of the health systems. Nevertheless, there are some of the issues that are associated with AI in clinical practice they include; Data privacy and protection, Issue of bias in machine learning algorithms, the black challenge. Some of the professional factors that are important when implementing artificial intelligence are: equity non –discrimination and patient engagement. Nonetheless, policies regarding AI in the healthcare context are still in their development process, and about responsibility or purse or assurance has been put forward, and it involves security. The future of AI in healthcare is consequently expected to continue evolution of precision medicine, enhance the speed of drug development, as well as enhance patient satisfaction through smart health including next generations of virtual assistants and telemedicine. For AI to be used in a way that optimally takes advantage of the potential that exists in significantly improving health care delivery as well as productivity, the technological, ethical, and regulatory factors that characterize the technology’s application need to be tamed cohesively to realize safe uses that are patient centered. Heated debates over the life of the next few decades are required to ensure great advancements in the future of AI in healthcare, but these advancements need to be managed carefully to safeguard the best interests of each patient/healthcare consumer and provider.