Messy Clinic Framework for Clinical AI Development

This technical report presents the Messy Clinic framework, which addresses the challenges of utilizing real-world clinical data for AI applications in healthcare. The report outlines the inherent complexities of healthcare data, including its noisy, incomplete, and heterogeneous nature, which poses significant challenges for AI models trained on curated datasets. It argues that traditional approaches often fail to account for the operational realities of clinical environments, leading to models that lack robustness and generalizability. The Messy Clinic framework proposes a unified data architecture that integrates multimodal, longitudinal, and privacy-preserving corpora, emphasizing the importance of preserving the authentic characteristics of clinical data. Key contributions include the integration of diverse data sources such as electronic health records, medical imaging, genomic data, and wearable sensor data, along with the adoption of federated learning to maintain patient privacy. The report advocates for a shift in AI modeling paradigms, promoting resilience to data messiness as a foundational principle for developing trustworthy healthcare AI systems.