From Algorithms to Outcomes: Transforming Modern Healthcare through Artificial Intelligence

Artificial Intelligence (AI) refers to the utilisation of computers and advanced technologies to simulate intelligent behaviour and critical thinking comparable to that of humans. The term was first described by John McCarthy in 1956 as the science and engineering of creating intelligent machines. [1,2]. Previously considered a concept of science fiction, AI is now a tangible reality and is widely represented within academic discussion and mainstream applications. Machine Learning (ML), which is a subset of AI, enables machines to learn from patient data and generate predictions by pattern recognition, thereby empowering healthcare providers in delivering better care through accurate diagnosis and treatments. Although current technologies and AI models have not yet advanced to a stage where they may replace a doctor, they hold considerable promise as valuable diagnostic tools in healthcare. [1,3] While the likelihood of AI assuming a significant role in healthcare seems imminent, its evolution is currently tempered by concerns regarding ethical challenges and patient safety. This literature review aims to examine the contemporary applications of AI in healthcare, its potential advantages for both patients and healthcare professionals, and the existing challenges and limitations that may hinder its continued progression. [1,2]

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.