THE IMPACT OF AI ON HEALTHCARE DIAGNOSIS

Abstract

Artificial intelligence (AI) became a fundamental component of the modern healthcare system, and it is altering the process of diagnosing patients in numerous ways. There is a rapid development of machine learning (ML) and deep learning (DL) technologies that enables automated systems to analyze clinical, imaging and physiological data with greater accuracy in comparison to humans. In this paper, the recent literature about the impact of AI on the diagnostic decision in the field of healthcare is described, both in terms of imaging and data driven diagnostic application. We will review the evidence on AI to enhance the diagnostic accuracy, efficiency and consistency of AI in the following medical domains: radiology, oncology, ophthalmology, hepatology, and critical care medicine, through a review of the most recently published systematic reviews and original studies. Moreover, we will comment on multimodal and personalized diagnostic strategies, explainable AI, and creating deployment models like edge AI. Although a lot has been accomplished, a lot remains of the work to be done in areas of data quality, bias, interpretability, regulatory oversight, and linkage to real world practice. Thus, we can conclude that despite the high potential of AI in assisting diagnostic decision-making, it must undergo a high-quality validation process, be ethically implemented, and well-integrated with human clinicians to ensure the long-term effect on patient care.