Article: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(26)00036-1/fulltext?rss=yes

Second article: https://www.thelancet.com/journals/landig/article/PIIS2589-7500(26)00030-0/fulltext?rss=yes

It sounds like the problem is the lack of standards around the models themselves, although that is more difficult to solve. My gut says that a single modality trained model will always out perform a generalist one. Excerpt here:

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. The study represents an important step towards a generalist AI framework that is capable of supporting cross-disciplinary imaging interpretation.1

Most existing medical imaging AI models have been developed as task-specific tools, typically focusing on a single modality such as chest radiography, pathology slides, retinal photographs, or CT imaging.2,3 Although these specialist models can achieve excellent performance in narrow tasks, their fragmentation creates challenges for real-world use.4 Hospitals often need to maintain multiple AI systems across departments, each requiring separate validation, governance, and technical integration. Zhou and colleagues attempt to address this limitation by developing a unified vision-only foundation model that can interpret diverse medical images within a single architecture. By pretraining the model on large-scale multi-institutional datasets covering radiology, pathology, ophthalmology, dermatology, and ultrasound and combining self-supervised learning with a memory-based training mechanism, MerMED-FM demonstrates competitive performance across multiple clinical imaging benchmarks. The conceptual transition from fragmented specialist models to generalist medical foundation models is shown in the figure.