Artificial intelligence in biomedical research

Abstract

I am evaluating the feasibility of making predictions on COVID-19 patient treatment outcomes using data from various measurements.My impression is that, compared to traditional statistical methods, artificial intelligence (AI) results in better predictions.I am wondering what the pros and cons of the AI methods are.First introduced by Alan Turing, who is often referred to as the "father of computer science", and then later defined by John McCarthy, AI is "the science and engineering of making intelligent machines, especially intelligent computer programs.It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." 1,2 is a rapidly developing field, and encompasses a number of subfields and applications, including machine learning (ML), natural language processing, computer vision, robotics, and expert systems. 3Among them, ML is the subfield that has the most applications in the biomedical field.Compared to traditional statistical methods, ML methods often dominate accuracy benchmarks and achieve substantially better results.On the other hand, the improved predictive accuracy is associated with increased model complexity, which results in increased difficulty in interpretation. 4

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