In this episode of Beyond Compliance, Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, speaks with Joseph Geraci, PhD, co-founder and chief scientific and technical officer at NetraMark, about why explainable AI, not black-box prediction, is needed to reveal clinically meaningful patient subgroups in regulated drug development.
In this episode of Beyond Compliance, host Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, talks with Joseph Geraci, PhD, co-founder and chief scientific and technical officer at NetraMark, about how averaging patients into broad disease categories can obscure real treatment effects and hide the subgroups most likely to respond to a therapy.
Geraci, a mathematician whose work spans quantum computation, oncology, and neuroscience before founding NetraMark, argues that most drug failures aren't due to bad science but to trials that fail to account for biological heterogeneity, leaving real economic and clinical value on the table. He explains why black-box AI models, built on the same averaging techniques that power large language models, are poorly suited to clinical trial data and difficult to defend to biostatisticians, clinicians, and regulators. NetraMark's alternative approach, he says, is built to recognize when it doesn't have enough information, allowing it to surface model-derived patient subgroups characterized by variables clinicians can recognize and act on.
Geraci also discusses how insights from earlier-phase trials can be transported into later, more expensive phases, and how this approach could inform adaptive trial designs. He closes with a call for balancing broad, economically favorable drug labels against the biological reality of who a therapy actually helps.
