Transforming Trial Design and Patient Data with Deterministic AI - with Emma Vitalini of Amgen

**Matthew D'Amelo** (0:17)
Welcome everyone to the AI in Business Podcast. I'm Matthew D'Amelo, Editorial Director here at Emerge AI Research. Today's guest is Emma Vitalini, Head of Global Digital Health Technology Innovation at Amgen. Emma joins us on today's show to discuss how AI-enabled, decentralized clinical trial technologies are reshaping patient recruitment, consent and study execution, particularly in addressing enrollment delays, diversity gaps and global scalability challenges. Our conversation also explores practical workflow and ROI implications for sponsors, including using AI to surface unstructured patient data for faster screening, reducing screen fail rates through wearables and remote monitoring, and deploying modular plug-and-play platforms that balance regulatory compliance with speed. Emma also shares how leaders can start small with low-risk, high-value use cases, align early with compliance and legal teams, and measure success through accuracy, reduced rework, and time saved across trial operations. Just a quick note for our audience that the views expressed on today's show by Emma do not reflect that of Amgen or its leadership. But first, are you driving AI transformation at your organization? Or maybe you're guiding critical decisions on AI investments, strategy, or deployment? If so, the AI in Business Podcast wants to hear from you. Each year, Emerge AI Research features hundreds of executive thought leaders, everyone from the CIO of Goldman Sachs to the head of AI at Raytheon, and AI pioneers like Yoshua Bengio. With nearly a million annual listeners, AI in Business is the go-to destination for enterprise leaders navigating real-world AI adoption. You don't need to be an engineer or a technical expert to be on the show. If you're involved in AI implementation decision-making or strategy within your company, this is your opportunity to share your insights with a global audience of your peers. If you believe you can help other leaders move the needle on AI ROI, visit emerj.com and fill out our Thought Leader submission form. That's emerj.com and click on Be an Expert. You can also click the link in the description of today's show on your preferred podcast platform. That's emerj.com/expertone. Again, that's emerj.com/expertone. Without further ado, here's our conversation with Emma just after these quick messages.
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**Marilie Fouche** (3:13)
Emma, welcome to the show, so happy to have you on here today.

**Emma Vitalini** (3:16)
Thank you so much for inviting me to join you.

**Marilie Fouche** (3:19)
Emma, clinical trials are notoriously long, costly and full of uncertainty. One of the biggest challenges is spotting safety or efficacy signals early enough to make a real difference. When feedback comes too late, it not only delays treatments, but can impact patient outcomes. That's why the pressure to speed up trials without compromising quality has never been greater. Right now, AI tools like Co-Pilot Chatbots and EHR Extraction are starting to deliver on that promise, helping teams catch problems sooner and cut months of trial timelines. Emma, clinical trial teams face major hurdles in quickly detecting safety concerns or efficacy signals. From your perspective, what are the biggest challenges slowing this process today?

**Emma Vitalini** (4:00)
Thanks for the question. I think there are many. The way we have obviously operated for many years is that we collaborate with a number of global clinical trial sites. We have many people involved in the process and the studies at each site, from study nurses, obviously HCPs or other health care professionals. And often data is collated on a periodic basis. So we're not seeing real time information. And I think this has obviously led to challenges over time, or opportunities for improvement and enhancement that we can now consider with the new technologies. Things like an outcomes study that is looking for really having every patient in every last report. Challenges often in the past were that we would only start to see this as the study neared an end, that maybe there were missing data or there were unclean data or incomplete data. So data quality has often been a challenge. And now, AI technologies, you have the opportunity potentially either on the end of the sponsor or even at clinical sites to enable people to check the completeness of records, so that we're not coming to an end and really rushing or further delaying the actual being able to close the study because we're contacting sites and asking for more information again. So I think this enables them for us to detect that early indication of concerns or opportunities. Obviously, information is often or data are often blinded, so you're not looking at the actual outcomes, but you're able to judge, do we have everything that we need much earlier in the process?