Is AI Pharma’s Golden Pill?
The pharmaceutical industry is on the cusp of an artificial intelligence (AI) revolution that promises to reimagine how new drugs are discovered, developed, and delivered to patients. This was the resounding message from a panel of pharmaceutical and technology leaders at a recent industry conference.
The panel, featuring Mike Devoy, Chief Medical Officer at Bayer, Jim Swanson, Chief Information Officer at Johnson & Johnson, Mati Gill, CEO of AION Labs, and Nicole Büttner, founder and CEO of Merantix Momentum, explored the current state of AI in pharma and its potential to accelerate innovation and ultimately improve patient care.
Educating on AI’s Potential
According to Swanson, a key role for technology leadership in enabling AI adoption is serving as “an educator, a business leader and a technologist, in that order.” This means helping business colleagues understand what AI technologies are capable of, focusing on delivering meaningful outcomes for the company and patients and building scalable technology solutions in an agile way.
“You can chase shiny technical objects all you want, you’re not going to get the outcome,” Swanson said. “You have to have a use case or outcome that’s material for your company or for your patients or healthcare that is meaningful.”
Leveraging Israel’s Tech Expertise
While Israel wasn’t always an obvious place for biopharma investment, the country is now well-positioned to drive AI innovation in the industry by marrying its deep tech expertise with pharma’s domain knowledge in scientific drug discovery and development.
“Israel does have very well-established expertise and an entrepreneurial mindset in building startups that can bring in cutting-edge technologies,” said Gill. AION Labs is working to foster this by bringing Israeli tech entrepreneurs and scientific experts from major pharma companies to tackle tangible use cases.
Some critical R&D needs that could benefit most from AI include discovering antibodies computationally to target currently undruggable diseases, simulating how drugs will work in humans to reduce animal testing and clinical trial failure rates, and running trials more efficiently. The key is to deeply understand the problem to determine exactly what needs to be developed and tested.
Advancing Medical Imaging & Patient Care
At Bayer, AI is already impacting medical imaging and radiology — helping radiologists be more efficient and accurate in their interpretations, improving clinical workflows, and enabling more personalised medicine by integrating imaging with other patient data like electronic health records and genomics.
“There’s good examples in, for example, patients who have suffered a stroke and in a very short timeframe, you need to decide what’s appropriate,” Devoy explained. “Is it a clot? Is there an intervention you should be making? And that can all be improved, speeded up, made more accurate and there’s good evidence now that you can improve patient outcomes.”
Establishing Trust & Confronting Risks
As AI becomes more deeply embedded in pharma, establishing trust and confronting risks around data privacy, bias, and ethics is paramount. Both Bayer and J&J have established bioethics councils with cross-disciplinary experts to advise on AI projects and develop guidelines for teams to follow.
Transparency with patients and physicians is critical, as is handling sensitive data responsibly and working to identify and mitigate potential biases. Engaging policymakers is also key to ensuring regulations focus on outcomes rather than the constantly evolving technology itself.
“Everything’s kind of benefit-risk. The benefit here far outweighs the risk,” Swanson said. “We have to be aware of those risks, but what I found is the hardest thing is not even the technology; technology’s complex, a lot of this is change management and getting people to think and operate very differently.”
Driving Organisational Change
Panellists agreed that the biggest hurdle with implementing AI is often not the technology but driving the organisational change needed for adoption. This requires purposeful change management, working with early promoters to demonstrate value, and helping the organisation reimagine its operations.
For example, J&J has used AI models to identify clinical trial sites likely to enrol patients faster, resulting in 2.5 times faster enrollment and achieving diversity targets six months sooner. But getting there required a willingness to approach the problem differently.
Looking Ahead
While challenges remain, the panellists were optimistic about AI’s potential to accelerate the pace at which new drugs reach patients in need over the next few years. Impactful applications on the near-term horizon include using AI to detect diseases like lung cancer earlier, predict patient outcomes, and recommend optimal treatments — bringing diagnosis, prediction and intervention together to improve patient results.
More transformational impact could come from using AI-enabled telehealth to expand access to the same quality of care globally. “Technology can democratise health equity around the world,” Swanson said. “If you’re a community caregiver in Africa, you have the same knowledge because technology can enable them…All of a sudden, you’re serving the world in a much more effective way.”
Realising this future will require technologists and domain experts to come together to reimagine processes rather than thinking incrementally. With patients waiting on new therapies, the speakers agreed there is no better or more exciting industry for those who love to learn and make an impact.
“The value is so much far greater than the risk of not doing,” Swanson said. With the right focus on outcomes, skills, governance and patient protections, AI could be the catalyst that enables pharma’s ultimate mission. “We want to transform healthcare…I don’t know how you do that without technology.”
