Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to AI-driven vulnerability management for legacy medical systems. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components that collects information pertaining to a legacy medical system. In various aspects, at least one of the computer executable components can further leverage an artificial intelligence model and Retrieval Augmented Generation to detect a vulnerability in the legacy medical system. In various instances, the system can further generate a mitigation strategy for correcting the detected vulnerability. In various aspects, at least one of the computer executable components can further implement the mitigation strategy, thereby mitigating the detected vulnerability.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to AI-driven data recovery and cybersecurity. In various embodiments, a system can be provided. The system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise a detection component that can detect an anomaly caused by a cyberattack. The computer executable components can further comprise an isolation component that can isolate, based on detection of the anomaly, one or more computing systems affected by the cyberattack.

Abstract: A patient monitoring system includes a camera array positioned around a medical imaging device comprising facial cameras with integrated infrared sensors configured to capture facial images from multiple angles and light detection and ranging (LiDAR) based depth sensors configured to generate three-dimensional body mapping data. A processor initializes the system by activating the cameras and sensors, establishes baseline facial expression and body position data, performs parallel real-time monitoring during a medical imaging scan of facial expressions using facial landmark detection and body movements using skeletal reconstruction, analyzes deviations from baseline data against notification and auto-pause thresholds, generates notifications to a technologist when notification thresholds are exceeded, automatically pauses the scan when auto-pause thresholds are exceeded, confirms return of the patient to an acceptable state, and resumes the scan.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to AI-driven vulnerability management for legacy medical systems. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components that collects information pertaining to a legacy medical system. In various aspects, at least one of the computer executable components can further leverage an artificial intelligence model and Retrieval Augmented Generation to detect a vulnerability in the legacy medical system. In various instances, the system can further generate a mitigation strategy for correcting the detected vulnerability. In various aspects, at least one of the computer executable components can further implement the mitigation strategy, thereby mitigating the detected vulnerability.

Abstract: Various systems and methods are provided for automatically generating a radiology report. A medical image of a region of interest of a subject may be received. A structure of the region of interest of the subject may be segmented using a segmentation model and classified using a classification model. The medical imaging including the classified structure may be displayed via a user device of a radiologist. Speech data of the radiologist related to the classified structure may be received from the user device. Text data corresponding to the speech data may be generated using a natural language processing model. A radiology report corresponding to the medical image may be generated in a predetermined format using an artificial intelligence model. The radiology report including the medical image and an annotation including the generated text data in relation to the classified structure may be displayed via the user device of the radiologist.