Top five takeaways from the 2024 Danaher Summit

Our keynote speakers and panelists had spirited, frank discussions about where AI stands today and what it will take to keep it moving forward for the greatest impact in promoting human health.

We’ll be sharing more about the Summit in the coming weeks, but here are some of the biggest ideas from this singular event. 

When it comes to implementing AI in biomedicine, we need to think not just about the technology, but how it will be integrated into existing healthcare systems and incentive structures. This means having important conversations about reimbursement, safety and trust. AI tools may be effective, but they won’t be adopted unless providers understand their value and trust their results. More work is needed to develop the value case for AI in clinical use and to rigorously establish its safety.

We also need to bear in mind the complex economics of healthcare, ensuring that we are building both the technology and the infrastructure to incentivize healthcare systems to invest in the future. 

AI has already shown immense promise in tremendously valuable areas of drug development, like identifying potential targets, repurposing existing compounds and modeling binding behavior. But we still have a long way to go in proving that AI-discovered and -developed drugs can make it all the way through clinical trials and to patients with greater success than those discovered and developed with standard methods. In particular, the chasm between how drugs work in the lab versus how drugs work in patients has yet to be crossed. This is the key difference between making processes more efficient versus making them more accurate–ensuring that as we run faster, we’re running toward the right targets.

Whether by giving providers more time with patients, by democratizing access to specialty care or by creating powerful “digital twins” that can predict how drugs will behave in real people, AI is a powerful tool in getting more patients the kind of care they need. Many models are showing promise in not just predicting lifetime risk for diseases, but pointing to when they might manifest, increasing our ability to shift to a preventative, rather than reactionary healthcare practice. In a counterintuitive twist, some AI tools are actually coaching caregivers  to behave with more empathy and compassion – the exact opposite of what we associate with machines. The end result is that patients feel more seen and heard and experience a better quality of care overall.

Biomedicine is about more than just discovering a therapeutic molecule: it also involves highly complex manufacturing and regulatory processes. AI tools are cutting down the process of generating the key documents that guide a drug’s progress from weeks down to seconds, but to keep up the pace of progress, we need to ensure that regulatory processes are catching up with how fast technology is moving forward. The possibility of shared tools, like synthetic control arms, can also help expedite clinical trials and get faster results without sacrificing safety or efficacy.