Thebrain prepares forlearning evenbefore interacting withtheenvironment, by refining and optimizing its structures through spontaneous neural activity that resembles random noise. However,the mechanism of such aprocess has yet to be understood, and it is unclear whether this process can benefit the algorithm of machine learning.

Whilesomerecent works propose to distill an ensemble model into a single model to reduce such costs,thereisstillaperformance gapbetween theensemble anddistilledmodels.

In the past, MI datasets were frequently proprietary, confined to particular institutions, and stored in private repositories. In this particular setting, there is a pressing need for alternative models of data sharing, documentation, and governance. Within this context,theemergence ofCommunityContributed Platforms (CCPs) presented a potential for the public sharing of medical datasets.

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

Despite this remarkable progress, there is a noticeable absence of datasets that combine electrocardiogram (ECG) data with question answering.