HeartBEiT is much more precise at highlighting areas of interest, in this case for diagnosing heart attacks . Credit: Augmented Intelligence in Medicine and Science Laboratory at the Icahn School of Medicine at Mount Sinaiand detail of ECG diagnoses, even for rare conditions with limited data. It interprets ECGs as language and outperforms traditional CNNs, highlighting specific ECG areas responsible for heart conditions.
algorithms for computer vision tasks. Such CNNs are often pretrained on publicly available images of real-world objects,” says first author Akhil Vaid, MD, Instructor of Data-Driven and Digital Medicine at the Icahn School of Medicine at Mount Sinai. “Becauseis specialized to ECGs, it can perform as well as, if not better than, these methods using a tenth of the data.
Mount Sinai is taking the field in a bold new direction by building on the intense interest in so-called generative AI systems such as ChatGPT, which are built on transformers—deep learning models that are trained on massive datasets of text to generate human-like responses to prompts from users on almost any topic. Researchers are using a related image-generating model to create discrete representations of small parts of the ECG, enabling analysis of the ECG as language.
Indeed, through its sophisticated new modeling architecture, the Mount Sinai team has greatly enhanced the manner and opportunities by which physicians can interact with the ECG. “We want to be clear that artificial intelligence is by no means replacing diagnosis by professionals from ECGs,” explained Dr. Nadkarni, “but rather augmenting the ability of that medium in an exciting and compelling new way to detect heart problems and monitor the heart’s health.