Major drugmakers are leveraging AI to quickly identify patients for clinical trials and to potentially reduce the number of participants needed. This approach can significantly accelerate drug development and save millions of dollars. For instance, AI tools can analyze vast amounts of health data to streamline patient recruitment, cutting the time it takes to enroll participants by up to 50%.

AI can transform the drug development process by enhancing efficiency and reducing costs. For example, companies like Amgen have reported that their AI tool, ATOMIC, can halve the time required to enroll patients in trials. Additionally, AI can help in predicting long-term risks based on real-world data, which can lead to fewer participants being needed in trials, further saving time and resources.

While AI offers significant potential, there are challenges to consider. For example, less than 25% of health data is publicly available for research, which can limit the effectiveness of AI tools. Moreover, there are concerns from regulators about the reliability of AI-generated external control arms in trials, as they may not account for unknown variables that could affect drug efficacy.