Abstract: Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.

Abstract: Methods and systems for determining a diagnostically unacceptable medical image. One system includes at least one electronic processor configured to receive a new medical image captured via a medical imaging device. The at least one electronic processor is also configured to determine a classification of the new medical image using a model developed with machine learning using training information that includes a plurality of medical images and an associated classification for each medical image, each associated classification identifying whether the associated medical image is diagnostically unacceptable, wherein the classification of the new medical image indicates whether the new medical image is diagnostically unacceptable.

Abstract: Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.

Abstract: Methods and systems for determining a diagnostically unacceptable medical image. One system includes at least one electronic processor configured to receive a new medical image captured via a medical imaging device. The at least one electronic processor is also configured to determine a classification of the new medical image using a model developed with machine learning using training information that includes a plurality of medical images and an associated classification for each medical image, each associated classification identifying whether the associated medical image is diagnostically unacceptable, wherein the classification of the new medical image indicates whether the new medical image is diagnostically unacceptable.

Abstract: Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE maps the terms or concepts of interest to medical concepts in a medical knowledge base. The MISE processes electronic medical records (EMR) of the patient based on the mapping of the medical concepts in the medical knowledge base to the terms or concepts of interest in the summarization template to extract patient information from the patient EMR that matches at least one of the medical concepts from the mapping. The MIE generates and outputs a holistic summary of the patient's EMRs that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient.

Abstract: Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE expands the summarization template based on related concepts or related terms related to the terms or concepts of interest specified in the summarization template. The MISE processes an EMR of the patient based on the expanded summarization template to extract patient information corresponding to the terms or concepts of interest and the related concepts or related terms. The MISE generates and outputs a holistic summary of the EMR of the patient that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient, based on extracted patient information obtained from processing the patient EMR.