Towards a unified foundation model for medical imaging

Artificial intelligence (AI) has rapidly transformed medical imaging, enabling automated detection, classification, and risk prediction across an increasing number of clinical applications. However, in clinical practice, physicians routinely integrate information across imaging modalities, specialties, and diagnostic contexts. In their Article published in The Lancet Digital Health, Yang Zhou and colleagues introduce MerMED-FM, a multimodal, multidisease medical imaging foundation model trained on more than 3·3 million images across seven imaging modalities and more than ten clinical specialties.