Abstract: Systems and methods for automatic tagging of images and video in surgical streams are described. A plurality of machine learning models, trained on annotated surgical data, are used to extract salient images and video clips from surgical video streams. In addition, speech transcription models process audio streams to generate transcriptions that are then associated with the tagged media. Subsequently, the system synchronizes the multimodal data and generates structured operative records. After synchronization, billing rules are applied to produce accurate billing reports. Applications of the system include improving surgical documentation, reducing administrative burden, enhancing billing accuracy, and accelerating revenue cycles in healthcare environments.

Abstract: Systems and methods for automatically extracting one or more salient images from a surgical video stream are described. A plurality of records including annotated images from recorded surgical procedures are used as training data to generate an image extraction machine learning model. Features, extracted from the training data, are used as inputs to the image extraction machine learning model in a training phase, which outputs salient images. After training, features extracted from the surgical video stream are input into the trained image extraction machine learning model to output the one or more salient images from the surgical video stream.

Abstract: Systems and methods for automatically extracting one or more salient images from a surgical video stream are described. A plurality of records including annotated images from recorded surgical procedures are used as training data to generate an image extraction machine learning model. Features, extracted from the training data, are used as inputs to the image extraction machine learning model in a training phase, which outputs salient images. After training, features extracted from the surgical video stream are input into the trained image extraction machine learning model to output the one or more salient images from the surgical video stream.