THE EFFECTIVE EVALUATION OF AI-POWERED DATA COLLECTION APPLICATIONS

Abstract

After the advent of AI and analytics, healthcare quickly became recognized as one of the most promising sectors for their use. Integrating efficient algorithms from several areas of IT, advances in artificial intelligence (AI) and analytics are bringing about remarkable changes in the medical field and healthcare. There is little doubt that a great deal of work is produced each year in a wide range of academic institutions and innovation hubs throughout the globe, but advancement in their effective success is a source of concern. In healthcare, there is a growing body of evidence demonstrating the positive effects of AIA implementation. The purpose of this paper is to provide the research community with the necessary context to advance this field further and to propose a framework that will help integrate diverse AIA technologies around patient needs in a variety of healthcare contexts, particularly for patients in chronic care who present the most complex comorbidities and care needs. In this work, we investigate and talk about the current uses of AI in medicine. In particular, the study zeroes in on the three fastest-developing applications of AI in healthcare: drug development, clinical trials, and care for individual patients. Efficient data monitoring processes are another area where artificial intelligence (AI) might be helpful. In addition, the results show that AI-assisted clinical trials can manage enormous data sets and generate reliable findings. Companies specializing in medical AI provide tools to aid patients in all their needs. Clinical intelligence also does analyses of patient medical records in order to help patients enhance their quality of life. The purpose of this paper, which covers methods for collecting data in the health sector, is to draw attention to the significance of such data and to encourage the more thoughtful use of data in all phases of developing AI.