mlsanityData to 14 Oct 2026

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

arXiv 2601.16414 ↗First posted 23 Jan 2026cs.LG cs.AIcited by 2

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Abstract (arXiv)

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 39x faster processing and 20x 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. Available at pip install pyhealth.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 57 of its 117 references that are in the map. First posted on arXiv 23 Jan 2026.

Builds on

57 of 117 references.

References · 57

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026LUNG-KGMM: Knowledge-Guided Multimodal Learning for Lung Cancer Incidence Predictioncited by 0
  2. 2026TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Modelscited by 0