There is growing enthusiasm for AI’s potential to predict drug-induced liver injury (DILI)—but while AI-based models bring value in recognizing patterns across large datasets, they often fall short when it comes to mechanistic understanding and physiological realism. In this new blog post, our experts outline 6 reasons traditional QST modeling provides more comprehensive and clinically actionable predictions of DILI risk than current AI-only models. Read it here: https://lnkd.in/gTcKb3dN
