Abstract: A method and system is provided for radiology peer review feedback and learning using artificial intelligence. The system includes an electronic processor configured to: receive a set of medical imaging exams with reading physician data and at least one peer review score, train a machine learning algorithm to predict a review score from the medical imaging exam and reading physician data, use the trained machine learning algorithm to represent the medical imaging exam and the reading physician data, store a history of medical imaging exams for a reading physician, receive newly-reviewed medical imaging exam data for the reading physician having a feature vector, find similar medical imaging exams in the history of the medical imaging exams by comparing the feature vector of the newly-reviewed medical imaging exam data with the feature vectors for the medical imaging exams for the reading physician, and provide common review feedback to the reading physician.

Abstract: A method and system is provided for optimizing radiology peer review exam selection using artificial intelligence. The system includes an electronic processor configured to: receive a set of candidate medical imaging exams with reading physician data, assign the medical imaging exams to at least one peer reviewer, receive peer reviewer data including scores and/or text for the assigned medical imaging exams, update a machine learning algorithm to optimize the assignment of medical imaging exams to the at least one peer reviewer using the received peer reviewer data.

Abstract: Systems and methods for generating electronic reports for medical images. One system includes an electronic processor configured to access a plurality of prior reports associated with a user and automatically generate a mapping using machine learning based on the plurality of prior reports. The mapping associates language included in the plurality of prior reports with at least one section of a report. The system stores the mapping to a memory, receives input from the user for an electronic report associated with a medical image, accesses the mapping from the memory, automatically determines a section in the electronic report associated with the input based on the mapping, and automatically inserts text into the section in the electronic report based on the input.

Abstract: Systems and methods for generating electronic reports for medical images. One system includes an electronic processor configured to access a plurality of prior reports associated with a user and automatically generate a mapping using machine learning based on the plurality of prior reports. The mapping associates language included in the plurality of prior reports with at least one section of a report. The system stores the mapping to a memory, receives input from the user for an electronic report associated with a medical image, accesses the mapping from the memory, automatically determines a section in the electronic report associated with the input based on the mapping, and automatically inserts text into the section in the electronic report based on the input.

Abstract: Systems and methods that allow transfer and display rules to be defined based on one or more of several attributes, such as a particular user, site, device, and/or image/series characteristic, as well as whether individual images and/or image series are classified as thin slices and/or based on other characteristics, and applied to medical images in order to determine which images and/or image data are analyzed, downloaded, viewed, stored, rendered, processed, and/or any number of other actions that might be performed with respect to medical image data. The system and methods may include image analysis, image rendering, image transformation, image enhancement, and/or other aspects to enable efficient and customized review of medical images.

Abstract: Systems and methods that allow transfer and display rules to be defined based on one or more of several attributes, such as a particular user, site, device, and/or image/series characteristic, as well as whether individual images and/or image series are classified as thin slices and/or based on other characteristics, and applied to medical images in order to determine which images and/or image data are analyzed, downloaded, viewed, stored, rendered, processed, and/or any number of other actions that might be performed with respect to medical image data. The system and methods may include image analysis, image rendering, image transformation, image enhancement, and/or other aspects to enable efficient and customized review of medical images.

Abstract: Systems and methods that allow transfer and display rules to be defined based on one or more of several attributes, such as a particular user, site, device, and/or image/series characteristic, as well as whether individual images and/or image series are classified as thin slices and/or based on other characteristics, and applied to medical images in order to determine which images and/or image data are analyzed, downloaded, viewed, stored, rendered, processed, and/or any number of other actions that might be performed with respect to medical image data. The system and methods may include image analysis, image rendering, image transformation, image enhancement, and/or other aspects to enable efficient and customized review of medical images.

Abstract: Systems and methods that allow transfer and display rules to be defined based on one or more of several attributes, such as a particular user, site, device, and/or image/series characteristic, as well as whether individual images and/or image series are classified as thin slices and/or based on other characteristics, and applied to medical images in order to determine which images and/or image data are analyzed, downloaded, viewed, stored, rendered, processed, and/or any number of other actions that might be performed with respect to medical image data. The system and methods may include image analysis, image rendering, image transformation, image enhancement, and/or other aspects to enable efficient and customized review of medical images.