The article discusses how artificial intelligence (AI) is being used to accelerate and optimize clinical trials, addressing the challenges of drug development that have led to longer times and higher costs. AI is being applied in various stages of clinical trials, from trial design to patient recruitment and data analysis. Researchers are using AI to predict trial success, optimize eligibility criteria, and improve patient recruitment and retention. For example, algorithms like HINT and SPOT can predict trial outcomes based on drug molecules, target diseases, and patient eligibility criteria. AI tools such as SEETrials and ClinIDigest help in designing trials by extracting information from clinical trial abstracts and summarizing records from ClinicalTrials.gov. AI is also used to relax eligibility criteria while maintaining safety, reducing the number of patients needed for trials, and predicting patient drop-out rates. Additionally, AI can assist in data management, generating case report forms, and providing patient support through chatbots. However, ethical and practical challenges, such as bias and data privacy, must be addressed. Despite these challenges, the integration of AI in clinical trials is showing significant promise, with companies like Saama, Intelligent Medical Objects, and Unlearn leading the way.The article discusses how artificial intelligence (AI) is being used to accelerate and optimize clinical trials, addressing the challenges of drug development that have led to longer times and higher costs. AI is being applied in various stages of clinical trials, from trial design to patient recruitment and data analysis. Researchers are using AI to predict trial success, optimize eligibility criteria, and improve patient recruitment and retention. For example, algorithms like HINT and SPOT can predict trial outcomes based on drug molecules, target diseases, and patient eligibility criteria. AI tools such as SEETrials and ClinIDigest help in designing trials by extracting information from clinical trial abstracts and summarizing records from ClinicalTrials.gov. AI is also used to relax eligibility criteria while maintaining safety, reducing the number of patients needed for trials, and predicting patient drop-out rates. Additionally, AI can assist in data management, generating case report forms, and providing patient support through chatbots. However, ethical and practical challenges, such as bias and data privacy, must be addressed. Despite these challenges, the integration of AI in clinical trials is showing significant promise, with companies like Saama, Intelligent Medical Objects, and Unlearn leading the way.