ARTIFICIAL INTELLIGENCE FOR CLINICAL TRIALS

Abstract

Artificial Intelligence (AI) in healthcare is the usage of a complex set of algorithms that help to conduct systematic analysis of medical data. AI helps in reducing medical errors, digital acquisition, correcting algorithm mistakes, reading scientific literature and electronic record keeping at its ultra-fast computing speeds plays a major role in the clinical field. AI being a machine simulation of human intelligence processes includes learning, reasoning and selfcorrections by using several methods such as machine learning, deep learning and natural language processing while the ultimate goal is to build a smart machine capable of learning and comprehending. Clinical trials are an area with great potential for optimization. Only 12% of drug development programs had success in 2000-2019 study. AI simplifies the lives of patients, doctors and hospital administrations by performing tasks that are typically done by humans but by consuming less time and work. Incomplete medical histories and large caseloads that are not systematically handled can lead to deadly human errors that affect patients. Few causes such as suboptimal patient selection, recruiting techniques, inability to monitor patients effectively during trials are what leads to high trial failure rates. The high failure rates of clinical trials contribute substantially to the inefficiency of the drug development cycle. This article aims at discussing in detail how recent advances in artificial intelligence can be used to reshape key steps of clinical trial design towards increasing trial rates.