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Machine learning (ML) in healthcare presents numerous opportunities forenhancing patient care, population health, and healthcare providers' workflows.However, the real-world clinical and cost benefits remain limited due tochallenges in data privacy, heterogeneous data sources, and the inability tofully leverage multiple data modalities. In this perspective paper, weintroduce "patchwork learning" (PL), a novel paradigm that addresses theselimitations by integrating information from disparate datasets composed ofdifferent data modalities (e.g., clinical free-text, medical images, omics) anddistributed across separate and secure sites. PL allows the simultaneousutilization of complementary data sources while preserving data privacy,enabling the development of more holistic and generalizable ML models. Wepresent the concept of patchwork learning and its current implementations inhealthcare, exploring the potential opportunities and applicable data sourcesfor addressing various healthcare challenges. PL leverages bridging modalitiesor overlapping feature spaces across sites to facilitate information sharingand impute missing data, thereby addressing related prediction tasks. Wediscuss the challenges associated with PL, many of which are shared byfederated and multimodal learning, and provide recommendations for futureresearch in this field. By offering a more comprehensive approach to healthcaredata integration, patchwork learning has the potential to revolutionize theclinical applicability of ML models. This paradigm promises to strike a balancebetween personalization and generalizability, ultimately enhancing patientexperiences, improving population health, and optimizing healthcare providers'workflows.

Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources