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Medical data poses a daunting challenge for AI algorithms: it exists in manydifferent modalities, experiences frequent distribution shifts, and suffersfrom a scarcity of examples and labels. Recent advances, including transformersand self-supervised learning, promise a more universal approach that can beapplied flexibly across these diverse conditions. To measure and drive progressin this direction, we present BenchMD: a benchmark that tests how well unified,modality-agnostic methods, including architectures and training techniques(e.g. self-supervised learning, ImageNet pretraining),perform on a diversearray of clinically-relevant medical tasks. BenchMD combines 19 publiclyavailable datasets for 7 medical modalities, including 1D sensor data, 2Dimages, and 3D volumetric scans. Our benchmark reflects real-world dataconstraints by evaluating methods across a range of dataset sizes, includingchallenging few-shot settings that incentivize the use of pretraining. Finally,we evaluate performance on out-of-distribution data collected at differenthospitals than the training data, representing naturally-occurring distributionshifts that frequently degrade the performance of medical AI models. Ourbaseline results demonstrate that no unified learning technique achieves strongperformance across all modalities, leaving ample room for improvement on thebenchmark. Code is released at https://github.com/rajpurkarlab/BenchMD.

BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors