Background: Electronic health records (EHRs) have contributed to increased workloads for clinicians. Ambient artificial intelligence (AI) tools offer potential solutions, aiming to streamline clinical documentation and alleviate cognitive strain on healthcare providers.
Objective: To assess the clinical utility of an ambient AI tool in enhancing consultation experience and the completion of clinical documentation.
This paper introduces an Associative List Memory (ALM) that has high recall fidelity with low memory and low processing requirements. This permits a simple implementation in software on a personal computer or space instrument microprocessor. Associative List Memory has a performance comparable with Sparse Distributed Memory (SDM) but differs from SDM in that convergence occurs during learning, rather than on recall, and in that the memory is in the form of a dynamic list rather than static randomly distributed locations.
View Article and Find Full Text PDF