All Competitors

Every biological foundation model, evaluated and ranked by the bio.rodeo team

Showing 1–8 of 8 filtered models

  • GEM (Grounded ECG understanding with Multimodal LLM)

    19141304
    National University of Singapore +1 otherMarch 8, 2025cardiologyclinical_reasoningdiagnosis_grounding+7

    Multimodal LLM unifying 12-lead ECG time series, ECG images, and text for grounded, clinician-aligned electrocardiogram interpretation.

    BiosignalsLanguage model
    79Openness

  • ECG-LM

    —42—
    Tsinghua University +1 otherFebruary 4, 2025cardiologycardiovascular_disease_detectioncnn+6

    Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.

    BiosignalsLanguage model
    24Openness

  • ECGFM-KED

    4352—
    Shanghai Jiao Tong UniversityDecember 18, 2024cardiologycnncontrastive_learning+7

    Knowledge-enhanced ECG foundation model aligning a ResNet encoder with LLM-generated disease descriptions for zero- and few-shot interpretation.

    Biosignals
    30Openness

  • PULSE

    67301.9K
    The Ohio State University +1 otherOctober 21, 2024cardiologyecgecg_interpretation+6

    Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.

    BiosignalsImaging
    84Openness

  • ECGFounder

    14234121
    Peking University +2 othersOctober 5, 2024arrhythmia_detectioncardiologycnn+6

    Convolutional ECG foundation model trained on expert annotations spanning 150 diagnostic categories, with 12-lead and single-lead wearable variants.

    Biosignals
    75Openness

  • ECG-Chat

    8248—
    China University of Geosciences +2 othersAugust 16, 2024cardiologycontrastive_learningdisease_classification+6

    Multimodal ECG-language model aligning 12-lead waveforms with clinical report text for conversational cardiac diagnosis and report generation.

    BiosignalsLanguage model
    27Openness

  • ECG-FM

    30096—
    University of Toronto +1 otherAugust 9, 2024cardiologycontrastive_learningdisease_classification+6

    Open transformer foundation model for 12-lead electrocardiograms, pretrained on 1.5 million unlabeled ECGs with a wav2vec 2.0 self-supervised recipe.

    Biosignals
    67Openness

  • HeartBEiT

    25118—
    Icahn School of Medicine at Mount SinaiJune 6, 2023cardiologydisease_diagnosisecg_classification+4

    Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.

    Biosignals
    30Openness