Computer Vision and Healthcare

Cross-domain applications. Most research in this space isolates computer vision to medical imaging, but additional work highlights the use of cross-domain applications for environmental hazard detection in the hospital environment and the observation of clinical facility monitoring (for example, patient fall detection and staff hygiene compliance). This ecosystem-wide perspective redefines the presence of computer vision as a tool for overall healthcare optimization. One recent study of a cross-domain transfer module proposes to transfer natural vision domain features to the medical image domain, facilitating efficient fine-tuning of models pre-trained on large datasets.

AI-driven surgical skill assessment. By capturing and analyzing surgical procedure video recordings, systems can identify patterns that indicate various skill levels and connect specific metrics to performance and outcomes, such as instrument movement, eye tracking, and error rates/time to completion, to revolutionize training protocols. A recent review of collective research determined that intraoperative AI data analysis has the potential for automated technical skill assessment.

Federated learning that preserves data privacy. With this emerging paradigm, data decentralization becomes more collaborative and ensures data safety through a distributed ML approach where multiple entities train a model utilized by numerous facilities without sharing their respective raw data.