Benchmark Biomedical Task

Benchmarking biomedical tasks focuses on evaluating the performance of machine learning models, particularly large language models (LLMs) and neural architecture search (NAS) methods, on various healthcare-related datasets. Current research emphasizes improving model robustness (e.g., handling variations in drug naming), developing efficient and accessible models for clinical use (e.g., lightweight multimodal models for radiology), and creating high-quality benchmarks for specialized imaging modalities (e.g., volume electron microscopy). These efforts aim to enhance the reliability and applicability of AI in biomedicine, ultimately improving diagnostic accuracy, treatment planning, and drug discovery.