EVALUATING AI-DRIVEN DIAGNOSTIC TOOLS FOR EARLY DETECTION OF RARE DISEASES

Abstract

Early diagnosis of the rare diseases is one of the most chronic problems in the contemporary healthcare systems because of little clinical awareness, variable symptom expression, and lack of specific diagnostic skills. Rare diseases are rare on an individual basis but are collectively prevalent in millions of people across the globe and are tend to be characterized by late diagnosis, misdiagnosis and high morbidity and mortality rates. Over the past few years, diagnosing tools developed by artificial intelligence (AI) have become the source of promising innovations, which can change the entire approach to early disease diagnosis by the use of high-quality data analysis, machine learning algorithms, and pattern recognition methods. These solutions utilize massive clinical data, medical imaging, and genomic data, as well as electronic medical records, to avert clinicians to reveal indicators of rare diseases in earlier phases.