Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising clinical research, offering unprecedented opportunities to enhance efficiency, accuracy, and patient-centricity in drug development.
Recent initiatives by regulatory bodies and industry leaders underscore the transformative potential of these technologies.
Regulatory Embrace of AI
FDA Commissioner Dr. Marty Makary has outlined a comprehensive vision to integrate AI across the agency’s operations. A pilot program demonstrated that AI could summarize extensive regulatory documents in minutes, significantly reducing review times. By June 30, 2025, the FDA aims to deploy AI-assisted tools to all scientific reviewers, streamlining the evaluation process.
Furthermore, the FDA’s Center for Drug Evaluation and Research (CDER) is consolidating its AI initiatives under a single council. This move aims to enhance coordination and oversight of AI applications in pharmaceutical research and regulatory review.
Industry Innovations and Collaborations
Lestter Cruz Serrano, Head of Global Medical Affairs at Cognizant, emphasizes the importance of collaboration in leveraging AI for clinical research. By partnering with technology firms, academic institutions, and healthcare providers, Cognizant aims to develop AI-driven solutions that improve patient recruitment, trial design, and data analysis.
Lestter states “if the latest models of AI and ML truly achieve and mature to what they promise to be, we might see late-stage clinical trials can become early-stage clinical trials, things that used to be early-stage clinical trials can be done in vitro which will be true testament of accelerated drug discovery and development”.
One notable advancement is the use of AI to create “digital twins”—virtual models of patients that can predict responses to treatments. This approach has the potential to reduce the need for extensive animal testing and expedite the clinical trial process.
Challenges and Considerations
While the integration of AI in clinical research offers numerous benefits, it also presents challenges. Ensuring data privacy, maintaining transparency in AI algorithms, and addressing potential biases are critical considerations. Moreover, regulatory frameworks must evolve to keep pace with rapid technological advancements.
The convergence of AI, regulatory support, and industry collaboration is poised to redefine clinical research. By embracing these technologies, stakeholders can accelerate drug development, enhance patient outcomes, and usher in a new era of personalised medicine.