Harnessing AI for more efficient clinical trials

Late in 2025 the US Food and Drug Administration (FDA) qualified its first artificial intelligence (AI) tool for use in clinical trials, one to help pathologists to analyse and score liver biopsy images. 

Although AIM-NASH (or the AI-Based Histologic Measurement of NASH) focuses on disease activity in clinical trials for metabolic dysfunction-associated steatohepatitis (MASH), the disease is far from being alone in potential areas of AI application. 

Indeed, from niche phase I clinical trials to major late-stage studies, artificial intelligence (AI) could have applications across all types of research and development projects. 

As the technology continues to be considered as a way to speed up tasks, reveal deep insights from the vast amounts of data generated and even predict the success of trials, industry attention is turned to more and more challenges in clinical research. 

From dealing with data overload to regulatory - and ethical – considerations, keeping pace with the ever-developing technology requires a multifaceted approach. 

Key learning points:

• Address governance, validation and auditability questions
• Turn data into actionable insights
• Gain advantage through genetic artificial intelligence

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