AI in clinical trials

Artificial intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence. Machine learning, a subset of AI, enables systems to learn from data and improve understanding over time without explicit programming. Generative AI builds on that foundation and can learn human language, programming languages, chemistry, biology or any complex subject matter.

Clinical trials are research studies involving human participants which aim to evaluate medical, surgical or behavioural interventions. They are essential for determining the safety and efficacy of new treatments, drugs and procedures before they become widely available.

The integration of AI into clinical trials is transforming the research landscape in several ways:

Machine learning in clinical trials can predict potential outcomes based on existing data, aiding in decision-making and risk assessment throughout the trial process.

AI assists in designing trials by analysing previous studies and real-world data to determine optimal sample sizes, endpoints and methodologies, thereby reducing costs and improving success rates.

During trials, AI monitors data in real-time, identifying anomalies or trends that may require attention.

AI can swiftly analyse electronic health records to identify suitable candidates for trials, enhancing recruitment efficiency and ensuring diverse participant representation. For instance, the US National Institutes of Health has developed an AI algorithm called TrialGPT that matches potential volunteers to clinical trials, improving enrolment processes.

The incorporation of AI into clinical trials offers numerous advantages:

Automating complex tasks accelerates trial processes, reducing the time required to bring new treatments to market.

By streamlining operations and minimising errors, AI reduces the financial burden associated with clinical research.

Early detection of adverse events through AI monitoring enhances participant safety and allows for prompt interventions.

The use of AI in medical research enhances the overall decision-making process, from trial design to data interpretation, leading to more effective and targeted therapies.

Recent breakthroughs in generative AI could further enhance trial simulations and predictive modelling. Moreover, ongoing research aims to improve AI’s capabilities in understanding complex biological systems, potentially leading to personalised medicine tailored to individual patient profiles.

According to the World Economic Forum, unlocking generative AI's full potential could save billions of dollars, while speeding up the delivery of lifesaving treatments. As technology advances, the collaboration between AI and clinical research is set to drive significant medical breakthroughs, ultimately benefiting patients worldwide.