An Overview and Methodical Strategy to Counteract the Medical Data Shortage for AI Applications

Abstract

Artificial intelligence (AI) has the power to improve healthcare systems. Additionally, AI has the potential to improve the accuracy and fairness of medical facilities. The amount of data we currently have is inadequate despite an increase in new data. There can be an issue in this area since some health-related diseases occur less frequently than others. The amount and complexity of health-related data limit our ability to collect this type of information since this collection is expensive and complicated. At times, there are either not enough subjects included in the study or, collectively across studies, there is a lack of subject numbers. As AI and health care evolve, the performance of an ML (machine learning) model will always be poor if it does not have sufficient/adequate amounts of data upon which to learn. As a result, the models may be biased and perform poorly in medical settings. This study looks at the problems caused by a lack of information in the healthcare sector. It talks about what this means and how we can fix these issues. The study also looks at how specialists in machine learning (ML) are addressing these issues and how these concepts can be used in medical settings and in the health sector through models of machine learning. This review aims to provide researchers looking to create trustworthy predictive models using ML for healthcare purposes with a useful resource.