Artificial Intelligence (AI) in life sciences refers to the use of advanced algorithms and computational systems to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Machine Learning (ML) is a key subset of AI where systems are trained on vast biological, clinical, and chemical datasets to identify patterns and make predictions without being explicitly programmed.
In the pharmaceutical industry, AI/ML is revolutionizing the entire value chain. In Drug Discovery, it can analyze millions of compounds to predict efficacy and toxicity, dramatically speeding up the identification of promising drug candidates (known as in silico screening). During Clinical Trials, AI optimizes patient selection, predicts which participants are most likely to respond to a treatment, and analyzes complex imaging and genetic data, leading to more efficient and shorter trials. The technology is a central driver of the industry’s shift towards digital transformation, lowering R&D costs, and accelerating the delivery of new therapies. Its ability to handle Real-World Data (RWD) and detect subtle safety signals also extends its utility into the post-marketing and pharmacovigilance phases.
