Training Small Multimodal Models to Bridge Biomedical Competency Gap: A Case Study in Radiology Imaging

arXiv:2403.08002v1 Announce Type: cross
Abstract: The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such large models in biomedicine. However, despite early promising results on some biomedical benchmarks, there are still major challenges that need to be addressed before these models can be used in real-world applications. Frontier models such as GPT-4V still have major competency gaps in multimodal capabilities for biomedical applications. Moreover, pragmatic issues such as access, cost, latency, and compliance …

abstract arxiv benchmarks biomedical biomedicine bridge case case study challenges cs.cl cs.cv development foundation gap however imaging large models laws major multimodal multimodal models performance radiology results scaling small study training type