Abstract: A report generating system is operable to generate inference data for a medical scan indicating a first subset of a plurality of anatomical features of the medical scan are normal. A set of default natural language text corresponding to the first subset of the plurality of anatomical features are identified based on report template data. Preliminary report data is generated to include the set of default natural language text corresponding to the first subset of the plurality of anatomical features based on the inference data. The preliminary report data is displayed an interactive user interface, and review data is received based on user input in response to at least one prompt displayed via the interactive user interface. Final report data that includes natural language text data for each of the plurality of report sections is generated based on the review data.

Abstract: A report generating system is operable to generate inference data for a medical scan indicating a first subset of a plurality of anatomical features of the medical scan are normal. A set of default natural language text corresponding to the first subset of the plurality of anatomical features are identified based on report template data. Preliminary report data is generated to include the set of default natural language text corresponding to the first subset of the plurality of anatomical features based on the inference data. The preliminary report data is displayed an interactive user interface, and review data is received based on user input in response to at least one prompt displayed via the interactive user interface. Final report data that includes natural language text data for each of the plurality of report sections is generated based on the review data.

Abstract: A system operates by receiving a plurality of medical scans, a plurality of medical labels corresponding to the plurality of medical scans and a plurality of confidence scores corresponding to the plurality of medical labels, wherein each of the plurality of medical labels correspond to one of a set of abnormality classes and wherein the plurality of confidence scores indicate a quantified representation of uncertainty generated via natural language processing of a plurality of medical reports corresponding to the plurality of medical labels; generating a computer vision model by training on the plurality of medical scans and the plurality of medical labels, wherein a model confidence of the computer vision model is calibrated based on the plurality of confidence scores; receiving a new medical scan; generating inference data corresponding to the new medical scan utilizing the computer vision model, wherein the inference data indicates an inferred abnormality in the new medical scan and the model confidence c