Timely and accurate MR protocoling is important to ensure best efficiency and diagnostic value in radiology departments. We propose and validate an artificial intelligence based natural language classifier that can assign MR abdomen/pelvis protocols based on free-text clinical indications. We achieve an overall classification accuracy rate of 93% on a test set consisting of 83 free-text clinical indications.
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