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The widespread presence of electronic health records and digitizing patient health data has led to various Clinical Decision Support (CDS) systems, leveraging novel analysis tools such as Machine Learning (ML). While current ML-based CDS systems are continually developed, they are frequently limited by the data they are trained on. We, therefore, propose a novel architecture called "BlockAI," which is designed to employ blockchain and smart contracts to continuously receive data from participating healthcare infrastructures to aid ML processes. With the help of smart contract based transactions, and incentive mechanisms, BlockAI allows sustainable machine learning upon a much more considerable set of healthcare data collected from healthcare institutions worldwide. As a motivating example, we have implemented BlockAI for sepsis mortality prediction and have compared the key functionalities of our …