This sounds great and all, but it's wishful thinking. There isn't anything in this supporting that it's able to find any meaningful patterns beyond existing solutions (i.e. standard rules based detection/machine learning as mentioned above).
What they've essentially done is taken a dataset in which each report was "annotated with a report string (generated by cardiologist or automatic interpretation by ECG-device)" [1] and used it with a series of templates (i.e. questions to ask the llm) from the ECG-QA paper [2] to fine-tune a model to achieve 65% accuracy with solely pattern recognition and 85% accuracy with pattern+clinical context (i.e. patient history).
The 42 template questions they used (as mentioned in 4.1 in the paper) can each be evaluated deterministically via code and retrieved via a tool call for any llm to parse. And I argue that the results would be the same, if not better, for a fraction of the cost. Doing calculations like this on time series data is very very quick. A couple ms at most. I don't see why this couldn't be run on the edge.
Plus, Table 9 shows this thing takes a minimum of 7GB of ram usage with a 270m parameter model and ~15-20GB for a 1B model. I don't see how this could be run on the edge considering most phones have 6-8GB of ram.
[1]: https://physionet.org/content/ptb-xl/1.0.3/ [2]: https://arxiv.org/pdf/2306.15681