Abstract: Methods and systems for clinical report generation. One system includes an electronic processor configured to receive a query image and determine a similarity metric for a plurality of medical images, where the similarity metric represents a similarity between the query image and each of the plurality of medical images. The electronic processor is also configured to determine a predetermined number of medical images from the plurality of medical images based on the similarity metric for each of the plurality of medical images. The electronic processor is also configured to rank a plurality of reports, where each of the plurality of reports correspond to one of the predetermined number of medical images. The electronic processor is also configured to generate a clinical report including information extracted from at least one of the plurality of reports based on the ranking of the plurality of reports.

Abstract: A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

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: A method, computer system, and a computer program product for automated report generation is provided. The present invention may include receiving a plurality of patient images. The present invention may include retrieving relevant data based on an analysis of the plurality of patient images. The present invention may include providing the relevant data to a user, wherein the relevant data provided to the user is in accordance with a relevant medical guideline. The present invention may include monitoring data input to generate a tailored medical report.

Abstract: A method, a computer system, and a computer program product for marketplace creation and model training in a blockchain network is provided. Embodiments of the present invention may include uploading images to a marketplace. Embodiments of the present invention may include recording references to the images on a blockchain ledger. Embodiments of the present invention may include measuring local and global annotations. Embodiments of the present invention may include determining an image quality threshold has been met. Embodiments of the present invention may include providing an annotation quality metric to the marketplace. Embodiments of the present invention may include pre-processing and standardizing the images. Embodiments of the present invention may include training a model. Embodiments of the present invention may include recording the model in the blockchain ledger. Embodiments of the present invention may include providing the model to the marketplace.

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: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to submit at least a portion of the image study to a cognitive system, the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.

Abstract: A method, computer system, and a computer program product for automatic labeling to train a machine learning algorithm is provided. The present invention may include labeling a medical image with at least one finding from a corresponding medical report. The present invention may include determining a localization information from the labeled medical image. The present invention may include training the machine learning algorithm with the determined localization information. The present invention may include detecting at least one candidate in a test medical image. The present invention may include generating a discrepancy list between the at least one detected candidate in the test medical image and at least one human-reported finding in a corresponding test medical report. The present invention may include, in response to determining that the generated discrepancy list is above a threshold, retraining the trained machine learning algorithm until the generated discrepancy list is below the threshold.

Abstract: Methods and systems for performing image analytics using graphical reporting associated with clinical images. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The model is used to automatically analyze an image.

Abstract: A method, computer system, and a computer program product for dynamically altering at least one image is provided. The present invention may include receiving a plurality of data, wherein the received plurality of data includes at least one existing medical image. The present invention may also include determining that one or more user instructions for the received existing image were received. The present invention may then include implementing the one or more user instructions on the received existing medical image. The present invention may also include altering the received existing medical image based on the one or more implemented user instructions and a medical knowledge base.

Abstract: Mechanisms are provided for implementing a patient complexity classification (PCC) computing system. The PCC computing system receives medical image study data for a patient that comprises one or more medical image data structures and one or more corresponding medical image metadata data structures. A natural language processing engine of the PCC computing system performs natural language processing on the medical image metadata data structure to extract features indicative of at least one of patient or medical image characteristics. A complexity classifier of the PCC computing system evaluates the extracted features to determine a patient complexity indicating a complexity of a medical condition of the patient. Routing logic associated with the PCC computing system routes the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity.

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: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from an imaging device. The image may be a medical image. The computing device may detect one or more potential indicators of disease in the image using a first algorithm and determine areas of potential disease in the image using an artificial intelligence algorithm. The computing device may determine a correlation between the determined areas of potential disease in the image and the one or more potential indicators of disease for the image. The computing device may, in response to determining a positive correlation, identify one or more of the potential indicators of disease for annotation and generate a report indicating one or more potential indicators of disease was found in the image.

Abstract: A computer system generates a clinical summary for a patient based on machine learning. One or more templates are generated, each indicating medical information for a corresponding clinical summary with respect to a medical condition of a patient. Preferences for medical information for each corresponding clinical summary are learned based on a history of desired medical information for clinical summaries for the medical condition. The learned preferences are applied to the one or more templates. A clinical summary is generated with respect to the medical condition of the patient based on the one or more templates with the learned preferences. Embodiments of the present invention further include a method and program product for generating a clinical summary for a patient based on machine learning in substantially the same manner described above.

Abstract: Methods and systems for using contextual information to generate reports for image studies. One method includes determining contextual information associated with an image study wherein at least one image included in the image study loaded in a reporting application. The method also includes automatically selecting, with an electronic processor, a vocabulary for a natural language processing engine based on the contextual information. In addition, the method includes receiving, from a microphone, audio data and processing the audio data with the natural language processing engine using the vocabulary to generate data for a report for the image study generated using the reporting application.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, and automatically generating a worklist based on the classification assigned to the image study using the model, the worklist prioritizing a plurality of tasks for treating the patient.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system comprises a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, automatically generate a differential diagnosis for the patient based on the classification assigned by the model, and automatically adjust triaging of the image study based on the differential diagnosis.

Abstract: Methods and systems for clinical report generation. One system includes an electronic processor configured to receive a query image and determine a similarity metric for a plurality of medical images, where the similarity metric represents a similarity between the query image and each of the plurality of medical images. The electronic processor is also configured to determine a predetermined number of medical images from the plurality of medical images based on the similarity metric for each of the plurality of medical images. The electronic processor is also configured to rank a plurality of reports, where each of the plurality of reports correspond to one of the predetermined number of medical images. The electronic processor is also configured to generate a clinical report including information extracted from at least one of the plurality of reports based on the ranking of the plurality of reports.

Abstract: Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system includes a computing device including an electronic processor. The electronic processor is configured to submit at least a portion of the image study to a cognitive system, the cognitive system configured to analyze the image study using a model developed using machine learning, receive, from the cognitive system, a BI-RADS classification assigned to the image study using the model, and automatically triage the image study based on the classification assigned to the image study by the cognitive system.