How Can AI Life Sciences Companies Stand Out at the Upcoming JP Morgan Healthcare Conference?

Pharmaceutical Executive: How can AI life sciences companies stand out at the upcoming JP Morgan Healthcare Conference?
Angela Schwab: There’s a few different ways. In 2025, we saw a lot of AI hype, with claims of using AI for everything and it solving all our problems. The disillusionment has set in, however, and to get out of AI what we really want, you must have a massive amount of prompting and back-and-forth conversations. With that, it still hallucinates.

The companies that are standing out are the ones with the deep, domain knowledge and are building processes into the AI step-by-step. No matter where they are, whether it’s the clinical-trial or life sciences landscape, they have that deep, domain knowledge to break apart all of the pieces of information and build it in a structured way where teams really succeed.

PE: How is AI supporting clinical trial design?
Schwab: We have a hit a new era of clinical trial design. Up until 2024, we’ve been doing this manually by taking a protocol that somebody else wrote and saying that it’s probably pretty good.

We copy-and-paste and put things together. What comes with that is the failures. There’s an incredibly high failure rate in clinical trials, such as not meeting end points, going massively over the timelines, and not getting patients in.

This compromises the integrity of the science and the business.

Those failures are inherent in that old manual process. Now, we have an opportunity where AI can scrape massive data sets and look for things like endpoints in other clinical trials in your domain or what other tests were done. It can provide that predictive quality into protocols and planning so you can start to understand things that will have a positive or negative impact on the development of the clinical trial.

PE: Can AI help solve the issue of high failure rates in clinical trials?
Schwab: There are many ways that clinical trials fail. Maybe it takes too long to find patients, subjects get overloaded, sites start disappearing because things are too complicated for them. The ways to make that failure rate go down are by putting more thought up front and thinking through the decisions in creating the protocol and how they impact patients, sites, the data being collected, and determining if there are ways of doing that better.

This could involve using decentralized elements that make it easier on patients and sites. Is it possible to decrease the data burden. We’ve been over collecting data, and a lot of it isn’t actually supporting the endpoints and objectives. When designing endpoints and protocol, there are many times you don’t need it to be so complex or have as many endpoints.

PE: How can teams reduce patient burden without weakening their science?
Schwab: Number one is lean trial design. We should never put patients through tests, visits, or questionnaires that do not directly support the trial’s goals.

As an example, I was working with a clinical team that wanted to do eight psychological questionnaires on their patients. That’s about two hours of questionnaires for the patients. The team said not to worry, because the patients would be sitting in the waiting room anyway.

Why are they in the waiting room for two hours? Do you think the assessments at the end of two hours of questionnaires is going to be valid. They’ll probably get angry and start filling out A for every answer.

We must put ourselves in the patients’ shoes and think about that journey. What’s it like, how far do they have to drive, could we do decentralized, could someone go to their home, could they do a questionnaire online, etc.

It’s worth doing a dry-run and having investigators go the site, see what the drive is like and even what parking is like. Sometimes we think about poor planning as something that can be expensive on patients and trial sites. We must be sure we’re reimbursing them correctly for their time and everything they’re putting into it.

PE: How are sites impacted by poor planning and trial design?
Schwab: The sites really bear the brunt of all of this. I worked at a site for about 14 years; I oversaw the determination of which trials to take on. A lot of those decisions weren’t necessarily based on the science, but instead on the operational impact of the trial.

We’d make the decision based on elements such as how many coordinators we’d need, how intense are the patient visits, and how intense the tests would be that impacted various departments. If a trial required 30 MRIs in a month, that could adversely affect multiple departments.

We must think through these plans step-by-step and not just assume that trial sites can handle the whole burden of any trial. They may say yes but later drop off due to the burden. If you have a clinical trial and sites seem excited, and all of the sudden you’re getting crickets, there’s probably a reason behind that.

PE: How can investors identify promising AI companies apart from generic ChatGPT wrappers?
Schwab: There are a few critical elements that investors should be looking for. There must be baked-in intelligence, it shouldn’t be something that looks like ChatGPT but claims to be just for one-specific thing. If it’s just for creating a document or Powerpoint, that’s likely a wrapper.

Built-in intelligence and infrastructure are in the place of an actual platform that uses AI to create specific content in specific ways. It walks the users through a journey in creating the correct document.

ChatGPT could write a protocol for a specific type of research study. But with our platform, it will work through everything step-by-step. It will go over the objectives, the hypothesis, the endpoints, and the procedures. All of those things stack on each other and are backed by data that the AI can pull in and help with the decision-making process.