The Role of Machine Learning in Clinical Research: Transforming the Future of Evidence Generation

The main purpose of this paper is to explore how machine learning is revolutionizing clinical research. A huge transition is about to occur in clinical development, thanks to the confluence of vast new digital data sources, computer capacity to discover clinically important trends in the data employing efficient artificial intelligence and machine-learning algorithms, and policymakers who are welcoming this transformation through new partnerships. There are different perspectives presented in this article, including those from academia, the biotechnology industry, non-profits, regulators, and technology firms on how to incorporate relevant computational data into clinical research and health care [1]. The use of machine learning (ML) in clinical trial design, conduct, and analysis has gained interest, but the evidence base has not been reviewed. Traditional randomized, controlled trials have become exponentially more difficult and expensive over time, and this is now threatening to hinder the development of novel treatments and technology in the future. Clinical trials may be transformed to be more pragmatic and efficient by using electronic health records, mobile apps, and wearable technologies, which are all on the rise [1]. Before these improvements can be widely deployed in randomized, controlled trials, several obstacles must be addressed