Consider this, an estimated 97 percent of health data generated in the world goes underutilised. Generative artificial intelligence (Gen AI) can crunch terabytes of data and provide actionable, human-like insights, in no time.

In a nutshell, the two statements explain the transformative potential of Gen AI in healthcare — a central theme at the recently concluded BioAsia 2024 (Hyderabad).

“The world has collected over 2.3 zettabytes of data (or 2 trillion DVDs of data) and about 97 per cent of it went underutilised. We are starting to sift through the data and bring opportunities from it with AI to use the benefits,” said Jeremy Jurgens, Managing Director, the World Economic Forum.

He was not the only one who underscored the importance of data crunching and deployment of AI to rev-up research in healthcare. Several industry heads at BioAsia also delved into the subject.

US-based pharma major Bristol-Myers Squibb (BMS) is among the early birds deploying AI to fast-paced research in drug discovery. Its Chief Executive Chris Boerner expressed excitement about the promise of AI and other digital tools that can help move faster and with a higher probability of success.

“It’s the accelerant that can help us deliver more medicines, even faster,” he says, giving examples from BMS. “With AI tools, we can write protocols for clinical trials much faster. Instead of months, we can do it in days,” he points out. The magnitude of its impact could determine whether a patient decides to enrol in a clinical trial or not. And for patients battling serious diseases like cancer, this could mean the difference between life and death, he said.

“To harness the real-world data to create virtual cohorts, collections of virtual patients enable us to reach decisions much faster. That means instead of recruiting 1,000 patients, we only need 500. Instead of taking six years, a trial might only take three years,” he explains.

Within research, says Boerner, AI is being used to make better molecules, faster. “In fact, all our small molecule trials will be enabled by AI by the end of this year. And again, all of this work is or will be happening right here in India,” he said.

Outlining how digital and computational tools were enabling BMS, he said, “We’re now moving beyond cancer and taking aim at autoimmune diseases with our next-generation technology. We can jump the gap by using AI models trained on thousands of patients to predict features, link to manufacturing success and to safety. And if successful, this will be groundbreaking,” he adds.

Models training humans

Diogo Rau, Eli Lilly’s Executive Vice-President and Chief Information and Digital Officer, points out, “Drug discovery is at the heart of AI forums. It is a very exciting time because our machines are truly generating molecules that no human would ever have imagined. It is not just how we train our models, but how our models train humans.”

“If we get this right, and figure out which comes out better, the combination of humans and machines can create incredible opportunities,” he says.

Pointing to a common concern, Sangita Reddy, Joint Managing Director at Apollo Hospitals, says that AI is not going to displace healthcare workers. In fact, it augments their work, she argues. Giving an in-house example of a platform they use, she says AI is analysing 5 million records through clinical intelligence. “When you embed that on top of every EMR (electronic medical record) data capture, you can increase accuracy,” she added.

A recent EY Parthenon and BioAsia report outlines the transformative impact AI and data can have on Indian pharma. Besides reducing the time to discover drugs, it would help the industry to cut down costs making it affordable for Indian companies to produce drugs, it pointed out.

“GenAI can predict bioactivity, toxicity and physiochemical properties. By offering the potential to lower early-stage drug development costs by 25 per cent to 50 per cent and accelerate the identification of failures, Gen AI can position Indian pharmaceuticals as innovators beyond the generics,” the report said.

On research benefits, it said, leveraging Gen AI can help industry “ambitiously construct a robust innovation pipeline, report early stage development cost savings and detect failure early on. With its capability to establish the right structure for drugs and make predictions related to bioactivity, toxicity, etc, GenAI can significantly contribute to target identification, predicting drug target interactions, compound generation, pharmacology analysis, drug formulation design and safety monitoring.”

Clearly, the benefits look attractive, with projections indicating that GenAI will contribute with $4-5 billion addition to the Gross Value Added (GVA) of the Indian pharma sector by 2030.