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Why can signals that appear consistent across many studies still reflect shared bias; how do sibling comparisons help recalibrate cumulative evidence; and what AI-enabled approaches can add to large-scale evidence integration? Viktor H. Ahlqvist, PhD, from the Karolinska Institute joins JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss why automated drug safety surveillance during pregnancy is urgently needed and how AI and computation can strengthen or undermine causal inference. Related Content:
By JAMA Network5
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Why can signals that appear consistent across many studies still reflect shared bias; how do sibling comparisons help recalibrate cumulative evidence; and what AI-enabled approaches can add to large-scale evidence integration? Viktor H. Ahlqvist, PhD, from the Karolinska Institute joins JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss why automated drug safety surveillance during pregnancy is urgently needed and how AI and computation can strengthen or undermine causal inference. Related Content:

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