AI does not just make clinical trials faster; t makes them smarter

Santhosh AF, VP, Asia Pacific South & India, Medidata highlights how AI is improving the efficiency and speed of the clinical trial process from early drug discovery to data analysis and decision-making. He also explains Medidata's contributions towards a digital transformation of the clinical research landscape, in an interview with Kavita Jani

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What are the major advancements in drug development research? What are the opportunities for India in the current landscape?

Drug development research has made remarkable progress globally, with India emerging as a key contributor. According to Deloitte, India's pharmaceutical research and development (R&D) market is estimated to expand to $130 billion by 2030, and the country is considered the third-largest global producer of drugs by volume").

The biggest shift we are seeing in drug development is the introduction and increasing adoption of advanced technologies, such as artificial intelligence (AI), data analytics, telemedicine, and wearables and sensor technologies. While still relatively in the early stages in India compared to other markets, we are already seeing great appetite on the ground and tremendous opportunities to revolutionise drug development research. Benefits of AI include accelerating insights by processing colossal amounts of data within seconds, a number that would take humans years to process, while telemedicine and wearables enable hybrid and decentralised trials which make them more accessible to participants.

What are the challenges in the current clinical trial landscape in India? How can they be tackled?

While the talent pool has grown, the industry still grapples with skill gaps and high attrition rates of around 30 per cent, higher than the global average of 12 per cent (1). One way to tackle attrition is through the incorporation of technology that will enable talent to do their work more efficiently, thereby helping to mitigate stress and burnout.

For example, AI can help automate time-consuming and traditionally manual tasks such as the collection, organisation, and analysis of data, minimising human error and providing real-time insights.