Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for a drone-based network vulnerability detection system. According to embodiments of the present invention, a drone receives routes and protocols for detecting and resolving network vulnerabilities. The drone identifies one or more electronic devices connected to one or more networks within an area of interest and detects one or more network vulnerabilities of the one or more electronic devices. If the drone detects a vulnerability, the drone updates a command center and identifies a resolution to the one or more network vulnerabilities. The drone then resolves the one or more network vulnerabilities based on the identified resolution.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

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 computer system manages administration of substances. Information is received including administration of one or more substances and user preferences for the administration. Features of the received information are extracted, and interactions between the one or more substances and a new substance for administration are identified based on the extracted features. A schedule is generated for administration of the one or more substances and the new substance based on the interactions. Embodiments of the present invention further include a method and program product for managing administration of substances in substantially the same manner described above.

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 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: 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 generate a structured report for the image study based on the classification assigned by the model, the structured report accessible by a radiologist via a structured reporting system.

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 computer system manages administration of substances. Information is received including administration of one or more substances and user preferences for the administration. Features of the received information are extracted, and interactions between the one or more substances and a new substance for administration are identified based on the extracted features. A schedule is generated for administration of the one or more substances and the new substance based on the interactions. Embodiments of the present invention further include a method and program product for managing administration of substances in substantially the same manner described above.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for a drone-based network vulnerability detection system. According to embodiments of the present invention, a drone receives routes and protocols for detecting and resolving network vulnerabilities. The drone identifies one or more electronic devices connected to one or more networks within an area of interest and detects one or more network vulnerabilities of the one or more electronic devices. If the drone detects a vulnerability, the drone updates a command center and identifies a resolution to the one or more network vulnerabilities. The drone then resolves the one or more network vulnerabilities based on the identified resolution.

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 generate a structured report for the image study based on the classification assigned by the model, the structured report accessible by a radiologist via a structured reporting system.

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 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.