Jorge Reis-Filho, Chief of AI for Science Innovation, Enterprise AI Unit at AstraZeneca, shared on LinkedIn:
”Yesterday, I had the immense pleasure of meeting Hannah Amies, Co-founder and CEO of Luvida, for a fascinating discussion centered around the ways AI can fundamentally reimagine clinical trials through a focus on understanding the intricate complexities of the person behind the patient.
Trial design and operational decisions have traditionally centered on biomedical and clinical data. What if behavioral, lifestyle, socioeconomic and environmental factors could be incorporated in a safe and responsible manner to improve recruitment, consent, retention and adherence? So many parameters can de facto influence who is identified as potentially eligible, who is able and willing to provide informed consent and who can remain engaged throughout a trial.
A richer layer of patient intelligence integrating behavioral and lifestyle data can help clinical teams identify and address barriers to participation at both the site and patient levels. We have already reached a point where highly capable AI can help clinical teams anticipate recruitment, retention, adherence and protocol risks before a study begins, when the opportunity to mitigate them is greatest.
Our conversation also explored a possibility I find immensely exciting. The responsible and safe deployment of extensively evaluated and benchmarked AI could help us confront long-standing biases in trial access and participation head-on, creating opportunities for broader and more representative patient populations to participate in, and potentially benefit from, clinical research.
Fully realizing this promise will require fit-for-purpose data, transparent governance and appropriate privacy protections throughout model development. Rigorous, fit-for-purpose evals and benchmarks that explicitly test performance across the populations we seek to serve will be a sine qua non, as will the involvement of AI scientists with a deep understanding of the social and socioeconomic determinants of trial participation and success, working closely with clinicians, trialists, epidemiologists, behavioral and social scientists and, most importantly, patient communities.
This is the type of AI-driven transformation clinical development needs: scientifically rigorous, grounded in patients’ lived realities and capable of providing data-driven decision support that fundamentally enables participation and breaks down barriers that have restricted participation for decades.
Hannah, thank you for sharing a mission and vision that are both intellectually compelling and profoundly human. I could not be more excited by the pace of AI-driven transformation and by its potential to make clinical trials more efficient, more representative and more responsive to the people they seek to serve.
Work of this nature will be essential as we build an AI-enabled clinical development ecosystem with patients at its very center.
Onwards and upwards!”
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