PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning

Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research. To address these challenges, we introduce PyHealth 2.0, an enhanced clinical deep learning toolkit that enables predictive modeling in as few as 7 lines of code. PyHealth 2.0 offers three key contributions: (1) a comprehensive toolkit addressing reproducibility and compatibility challenges by unifying 15+ datasets, 20+ clinical tasks, 25+ models, 5+ interpretability methods, and uncertainty quantification including conformal prediction within a single framework that supports diverse clinical data modalities—signals, imaging, and electronic health records—with translation of 5+ medical coding standards; (2) accessibility-focused design accommodating multimodal data and diverse computational resources with up to 39× faster processing and 20× lower memory usage, enabling work from 16GB laptops to production systems; and (3) an active open-source community of 400+ members lowering domain expertise barriers through extensive documentation, reproducible research contributions, and collaborations with academic health systems and industry partners, including multi-language support via RHealth. PyHealth 2.0 establishes an open-source foundation and community advancing accessible, reproducible healthcare AI.

Clinical AI research has enormous potential to improve healthcare, but building machine learning models that predict patient outcomes or recommend treatments remains difficult. Researchers must navigate complex medical datasets, high computational costs, and deep domain expertise just to reproduce existing results. PyHealth 2.0 is an open-source toolkit that removes these barriers within a single framework. It handles the full clinical AI lifecycle, from loading diverse medical data (electronic health records, clinical notes, images, and physiological signals) to training models and applying interpretability tools, in as few as 7 lines of code. Every pipeline step is standardized so that results are reproducible by anyone. A key advance is PyHealth 2.0's memory-efficient streaming design, which processes large hospital datasets in small chunks rather than loading everything into memory at once. A standard 16 GB laptop can now handle datasets that previously required a dedicated server, and in our benchmarks a MacBook Pro ran faster than our 32-core workstation thanks to faster local storage. Backed by an active community of 400+ members, PyHealth 2.0 makes clinical AI development accessible to researchers at institutions of all sizes.