Artificial Intelligence in Healthcare

Introduction

Health systems around the world are confronted with a number of issues, including rising disease load, multimorbidity, and disability brought on by aging and demographic transition, expanding demand for health services, rising societal expectations, and rising health expenditures (Jiang et al., 2017). Inefficiency and low productivity present another difficulty (Artificial Intelligence: Definition, Types, Examples, Technologies, 2022). Fiscal conservatism and misguided economic austerity measures that limit investment in healthcare are the backdrop to these difficulties facing the health sector. To overcome these obstacles and attain universal health coverage (UHC) by 2030, health systems must undergo fundamental restructuring. Machine learning, the most concrete use of artificial intelligence (AI), provides the potential of doing more with less and may serve as the impetus for such a change (What Is Super Artificial Intelligence (AI), 2022). It is also the newest development sector in digital technology. But neither the nature nor the scope of this promise have been systematically evaluated. The effect of digital technology on healthcare systems has been ambiguous so far (McCoy et al., 2022). Is AI the ingredient for such a transformation, or will it face the same fate as earlier attempts at introducing digital technology.