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: Methods and systems are provided for managing identifying information for an entity. The identifying information of the entity embedded in or associated with a digital image is detected, wherein the identifying information is selected from the group consisting of: text information and image information corresponding to one or more features of an entity. The text information may be removed from the digital image. The image information may be replaced with one or more computer generated synthetic images, wherein the computer generated synthetic images are based on a natural appearance of the digital image. The synthetic content, which may be generated by a GAN, is based on a natural appearance of the image. The medical image may also contain PHI in text-based fields associated with private tags/fields, which are automatically identified and removed using the systems and methods provided herein.

Abstract: Methods and systems are provided for managing identifying information for an entity. The identifying information of the entity embedded in or associated with a digital image is detected, wherein the identifying information is selected from the group consisting of: text information and image information corresponding to one or more features of an entity. The text information may be removed from the digital image. The image information may be replaced with one or more computer generated synthetic images, wherein the computer generated synthetic images are based on a natural appearance of the digital image. The synthetic content, which may be generated by a GAN, is based on a natural appearance of the image. The medical image may also contain PHI in text-based fields associated with private tags/fields, which are automatically identified and removed using the systems and methods provided herein.

Abstract: Consultation advice based on ongoing user monitoring is provided. In various embodiments, first physiological data of a user is collected by a wearable device for a first time period. From the first physiological data individualized physiological data statistics are determined. The individualized physiological data statistics are stored. Second physiological data of the user are collected by the wearable device for a second time period. The second physiological data are compared to the individualized physiological data statistics to detect an abnormality. The user is notified to seek medical attention.