Abstract: An embodiment for learning-based automatic selection of artificial intelligence applications. The embodiment may receive a user request for an exam, the user request including exam information. The embodiment may automatically identify an exam type cluster corresponding to the received exam information. The embodiment may automatically detect applicable AI applications corresponding to the identified exam type cluster. The embodiment may automatically run each applicable AI application on a series of relevant test sets to generate a score for each applicable AI application. The embodiment may automatically recommend to a user a highest-scoring applicable AI application.

Abstract: A computer system identifies a medical condition in a patient. A trained machine learning image generator is used to generate a set of training images based on three-dimensional patient imaging data, wherein each training image is labeled with a projection angle of the corresponding two-dimensional projection. Using the set of training images, a machine learning image classifier model is trained to identify patient rotation angles in x-ray images. X-ray images are processed with the machine learning image classifier model to identify patient rotation angles. A machine learning medical condition classifier model is trained to identify a medical condition using the labeled x-ray images. The machine learning medical condition classifier model determines an indication of the medical condition in a patient's x-ray image. Embodiments of the present invention further include a method and program product for identifying a medical condition in a patient in substantially the same manner described above.

Abstract: Computer technology for scheduling viewing of sets of medical images for evaluation of medical images (for example X-ray images) by a medical professional (for example, a radiologist). The scheduling is based on, at least in part, scheduling rules obtained from computerized analysis of historical viewing patterns of the medical professional and/or other similarly situated medical professional viewers. The scheduling may include various types of scheduling, such as time of day scheduling, date scheduling (that is, day of the week / month / year scheduling, order of images to be viewed within a set of patient images, order of viewing among and between the various patient sets of images and amount of time to be spent on each image, each set of images.

Abstract: A system may receive radiology data, outcome data for one or more exams, and elapsed time data for the one or more exams. The system may train a first machine learning algorithm to detect exams that are candidates for cherry-picking using the set of radiology data, outcome, and elapsed time data. The system may further determine, using the radiology data and the first machine learning algorithm, a likelihood that an exam was rejected due to cherry-picking.

Abstract: Methods and systems for assigning a medical image study for review. One method includes receiving a plurality of labeled medical image studies and one or more prior image studies of a patient associated with each of plurality of labeled medical image studies. The method also includes creating a set of training data including the plurality of labeled medical image studies and the one or more prior image studies received for each of the plurality of labeled medical image studies and training an artificial intelligence (AI) system using the set of training data. In addition, the method includes estimating, using the AI system as trained, a difficulty metric for an unlabeled medical image study based on the unlabeled medical image study and one or more prior image studies of a patient associated with the unlabeled image study and assigning the unlabeled medical image study for review based on the difficulty metric.

Abstract: Currently deployed models are replaced with new models when corresponding deployed algorithms are updated. The updated algorithm is pre-trained offline on training data used by the currently deployed model. Concurrent deployment of the pre-trained model during operation of the currently deployed model within the same AI system provides secondary training of the pre-trained model. For the same input, output of the currently deployed model is compared to output of the pre-trained model and a decreasing rewards process encourages matching output to that of the currently deployed model until a condition is met. Upon meeting the condition, the pre-trained model become the currently deployed model and the previously deployed model is no longer in use.

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: 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 computer system identifies a medical condition in a patient. A trained machine learning image generator is used to generate a set of training images based on three-dimensional patient imaging data, wherein each training image is labeled with a projection angle of the corresponding two-dimensional projection. Using the set of training images, a machine learning image classifier model is trained to identify patient rotation angles in x-ray images. X-ray images are processed with the machine learning image classifier model to identify patient rotation angles. A machine learning medical condition classifier model is trained to identify a medical condition using the labeled x-ray images. The machine learning medical condition classifier model determines an indication of the medical condition in a patient's x-ray image. Embodiments of the present invention further include a method and program product for identifying a medical condition in a patient in substantially the same manner described above.

Abstract: Methods and systems for training a model using machine learning for automatically distributing medical imaging studies to radiologists. One method includes receiving one or more medical images included in a medical study, each of the one or more medical images including image metadata defining characteristics of the corresponding medical image. The method further includes receiving radiologist metadata for each one of the plurality of radiologists, generating a state representation of the image metadata and the radiologist metadata, and providing the state representation to the model. The method further includes assigning, with the model, at least one of the one or more medical images to one of the plurality of radiologists, calculating feedback based on a change in the state representation after the at least one of the one or more medical images is assigned to one of the plurality of radiologists, and adjusting the model based on the feedback.

Abstract: A method for preemptively generating and rendering a view of a computerized image is provided. The method may include determining rendering parameters for the computerized image by predicting an action to be performed on the computerized image, wherein the action modifies a view of the computerized image, and wherein the determined rendering parameters are based on the modified view of the computerized image. The method may further include preemptively rendering the view of the computerized image that is based on the determined rendering parameters before the action is performed on the computerized image.

Abstract: Methods and systems for optimizing user interaction with a software application. One system includes an electronic processor configured to receive a collection of interaction data including a plurality of interaction sequences and data associated with each of the plurality of interaction sequences, determine a performance metric for each of the plurality of interaction sequences, and train, with the collection of interaction data and the performance metric determined for each of the plurality of interaction sequences, an artificial intelligence (“AI”) model using supervised learning. The electronic processor is also configured to receive a current interaction sequence of a user for the software application and generate, via the AI model as applied to the interaction pattern of the user, a recommendation, for display within a user interface, for a modified user interaction pattern of the user for the software application.

Abstract: One or more processors may identify a missing image in the set of multi-view images. Each of the images is associated with a particular view type. The one or more processors may generate, utilizing a replacement AI model, a replacement image for the missing image in the set of multi-view images. The replacement image is generated utilizing an AI model trained to generate a replacement image using training images from two or more time-adjacent sets of images. The one or more processors may identify a duplicate image in the set of multi-view images. The one or more processors may generate, utilizing a quality generative AI model, a characteristic improved image based on the duplicate image for the set of multi-view images. The one or more processors may output the replacement image and the characteristic improved image.

Abstract: One or more processors may identify a missing image in the set of multi-view images. Each of the images is associated with a particular view type. The one or more processors may generate, utilizing a replacement AI model, a replacement image for the missing image in the set of multi-view images. The replacement image is generated utilizing an AI model trained to generate a replacement image using training images from two or more time-adjacent sets of images. The one or more processors may identify a duplicate image in the set of multi-view images. The one or more processors may generate, utilizing a quality generative AI model, a characteristic improved image based on the duplicate image for the set of multi-view images. The one or more processors may output the replacement image and the characteristic improved image.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for performing a diagnostic assessment. The method may include receiving image data of a current screening image, prior screening image, current diagnostic image, or prior diagnostic image, each image corresponding to a view type. The method may include processing the image data using a trained model to determine first model output data; determining first diagnosis data associated with a first confidence score; determining patch data representing a difference between the prior screening image and the current screening image; identifying a location corresponding to the difference; and determining the patch data satisfies a condition.

Abstract: A method, computer system, and a computer program product for determining interruptibility is provided. The present invention may include gathering data about a task performed by a user. The present invention may include training a machine learning model based on the gathered data. The present invention may include determining a task estimate. The present invention may include tracking a task performance of the user in real time. The present invention may include determining an interruptibility of the user. The present invention may include providing the interruptibility of the user.

Abstract: Provided is a method for adapting an artificial intelligence (AI) model. The method includes comparing a distribution of a clinical data characteristic of a genuine dataset with a target distribution of the clinical data characteristic to identify any categories of the clinical data characteristic that are underrepresented in the genuine dataset. The method further includes generating an artificial test dataset based on the result of the comparison. The method further includes generating training data based on the artificial test dataset. The method further includes providing the training data to the AI model to adapt the AI model.

Abstract: Automated assignment of confidence levels to medical diagnoses in a machine learning training data with respect to annotations made upon review of medical records such as x-ray films and test results. Confidence levels support machine learning for computer-aided diagnostic activity.

Abstract: Image sequence analysis by receiving a set of sequential images associated with a timeline, determining a gap according to the set of sequential images, generating a synthetic image associated with the gap according to the set of sequential images, and providing a new set of images including the synthetic image.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for image artifact removal. The method, computer program product and computer system may include computing device which may receive a primary image and analyze the primary image for global artifacts and local artifacts. The computing device may, in response to identifying a global artifact in the primary image, generate a secondary image with the global artifact removed utilizing a first generative adversarial network. The computing device may, in response to identifying a local artifact in the primary image, generate a patch with the local artifact removed utilizing a second generative adversarial network. The computing device may generate a hybrid image containing a reduction of global artifacts and a reduction of local artifacts by combining the secondary image and the patch utilizing a hybrid generative adversarial network.