The medial collateral ligament of the knee is unique in that it has shown the capacity to heal with conservative measures. As a result, nonoperative treatment is often successful in returning patients back to previous levels of activity and function. However, specific surgical indications do exist for certain isolated and many combined medial collateral ligament injuries. Strict adherence to both nonoperative and operative principles allows for optimum treatment in most instances.
Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.
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