Abstract: Data is received that is generated by at least one sensor forming part of a surgical instrument. The sensor(s) on the surgical instrument can characterize use of the surgical instrument in relation to a patient. A first machine learning model can construct a force profile using the received data. The force profile includes a plurality of force patterns. In addition, a plurality of features are extracted from the received data. Thereafter, one or more attributes characterizing use of the surgical instrument are determined by a second machine learning model using the constructed force profile and the extracted features. Data characterizing the determination can be provided (e.g., displayed to a surgeon, etc.).

Abstract: A natural language request is received for surgical event information associated with one or more network-connected medical devices. Based on the natural language request, a generative artificial intelligence model is prompted to generate a database query. This database query is used to query a graph database which returns, data responsive to the database query. Such responsive information can be conveyed to a user initiating the request in a user interface (e.g., GUI, audio, etc.). In some variations, the results from the graph database are used to poll one or more other models to obtain further contextual information or to provide curation of the results in a more user-friendly and intuitive manner.

Abstract: Each of a plurality of edge computing devices are configured to receive data streams generated by at least one sensor forming part of a respective medical device (e.g., a sensor-equipped surgical tool, etc.) which, in turn, characterizes use of the respective medical device in relation to a particular patient. Each of the edge computing devices can execute at least one machine learning model which generates or from which model attributes are derived. The generated model attributes are anonymized using an anonymization technique such as k-anonymity. The anonymized generated model attributes are homomorphically encrypted and transmitted to a central server. Encrypted model attribute updates to at least one of the machine learning models are later received from the central server which results in the machine learning models executing on one or more of the edge computing devices to be updated based on the received encrypted model attribute updates.

Abstract: Data is received that is generated by at least one sensor forming part of a surgical instrument. The sensor(s) on the surgical instrument can characterize use of the surgical instrument in relation to a patient. A force profile segmentation model can construct a force profile using the received data. The force profile includes a plurality of force patterns. The force profile segmentation model includes at least one first machine learning trained using historical surgical instrument usage data. In addition, a plurality of features are extracted from the received data. Thereafter, one or more attributes characterizing use of the surgical instrument are determined by a force profile pattern recognition model using the constructed force profile and the extracted features. The force profile pattern recognition model includes at least one second machine learning model. Data characterizing the determination can be provided (e.g., displayed to a surgeon, etc.).