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  "path": "/t/best-latest-baseline-cardiology-model-available-today/174366#post_2",
  "publishedAt": "2026-03-18T05:34:08.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "the Hugging Science Discord",
    "Detailed version",
    "Google Research",
    "JMIR",
    "Google for Developers",
    "Hugging Face",
    "arXiv"
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  "textContent": "Since the topic relates to the medical field, I recommend also asking your question on the Hugging Science Discord. (Detailed version)\n\n* * *\n\nAs of **March 18, 2026** , the best **baseline** to start a **small cardiology use case** with is **MedGemma 1.5 4B Instruct**. Google released MedGemma 1.5 in **January 2026** as the updated medical model in the MedGemma line, and its Hugging Face collection was updated **6 days ago** , so it is still the most current major open medical foundation-model option in this family. (Google Research)\n\n## The direct recommendation\n\nUse **`google/medgemma-1.5-4b-it`** as your starting point. It is the best first baseline because it is a **medical-native** , **current** , **compute-efficient** , **multimodal** model that was explicitly positioned as an **adaptable starting point for healthcare developers** , not as a fixed demo model. Google’s own model documentation says MedGemma 1.5 4B is meant to be a compute-efficient starting point, small enough to run offline, and that developers are expected to fine-tune it for their specific use case. (Google Research)\n\n## Why this is the best first baseline\n\nCardiology is not one task. A “cardiology model” may need to handle **reports** , **EHR text** , **guidelines** , **patient instructions** , **scanned documents** , **ECG images** , or **raw ECG signals**. Broad reviews of clinical LLM use keep finding that there is **no single generalist model that works best across a wide range of clinical tasks** , and real deployment usually needs task-specific adaptation. That is why the best first choice is not the most exotic cardiology-specific model. It is the strongest **practical medical foundation model** that can be adapted cleanly. (JMIR)\n\nMedGemma 1.5 4B fits that role unusually well. Its published capabilities include **medical document understanding** , **EHR understanding** , improved medical text reasoning, and multimodal medical comprehension. Those are exactly the abilities that matter for many early cardiology projects such as discharge-summary QA, cardiology report extraction, registry abstraction, referral triage, guideline lookup, and scanned-note understanding. (Google for Developers)\n\nIts benchmark profile also looks like a good baseline profile rather than a narrow demo profile. On Google’s published evaluations, MedGemma 1.5 4B improved over MedGemma 1 4B on **MedQA** and **MedMCQA** , reached **89.6** on **EHRQA** , and showed strong document extraction results on raw PDF-to-JSON medical-report tasks, including **91.0 macro F1** on one internal raw-PDF evaluation. Those numbers do not mean it is “solved.” They do mean it is already strong in the exact workflow categories where small healthcare pilots usually begin. (Hugging Face)\n\n## Why not start with a larger model first\n\nIf you only care about **text** and have enough compute, **MedGemma 27B text-only** is stronger on text benchmarks. Google’s model card says it is trained exclusively on medical text, and its published numbers beat the 4B model on text tasks such as **MedQA** and **EHRNoteQA**. But that is not the same as being the best **baseline**. A baseline is supposed to be fast to test, cheap to adapt, and broad enough for the first iteration. The 27B model is better viewed as the upgrade path after the first prototype works. (Hugging Face)\n\nSo the rule is simple:\n\n  * **Best first baseline for most small cardiology projects:** **MedGemma 1.5 4B Instruct**. (Hugging Face)\n  * **Best next step if the project is purely text and you want more accuracy:** **MedGemma 27B text-only**. (Hugging Face)\n\n\n\n## The one big exception\n\nIf your cardiology use case is **raw ECG waveform modeling** , then MedGemma is not the right first choice. In that case, start with **ECGFounder** instead. ECGFounder was trained on **over 10 million ECGs** with **150 label categories** , was designed to work both out of the box and through downstream fine-tuning, and was built specifically for ECG analysis across multiple domains, including lower-rank and single-lead settings. For ECG classification, arrhythmia detection, or signal-level transfer learning, that is the more appropriate baseline. (arXiv)\n\n## What this means in plain English\n\nIf your first cardiology project looks like any of these:\n\n  * extract structured fields from cardiology reports,\n  * answer questions from cardiology notes or discharge summaries,\n  * build a guideline-grounded assistant,\n  * summarize or rewrite cardiology documentation,\n  * handle mixed text-plus-document inputs,\n\n\n\nthen start with **MedGemma 1.5 4B Instruct**. Its shape matches those tasks well, and recent cardiology RAG work also shows that strong retrieval plus a good medical base model is a very effective pattern for cardiology knowledge tasks. (Hugging Face)\n\nIf your first project is instead:\n\n  * ECG waveform classification,\n  * signal-level prediction,\n  * wearable/single-lead ECG transfer,\n  * ECG representation learning,\n\n\n\nthen start with **ECGFounder**. (arXiv)\n\n## Final answer\n\nIf you want **one model name** to begin with, use:\n\n**MedGemma 1.5 4B Instruct**\n\nThat is the best **latest practical baseline** for a small cardiology use case today because it is current, medical, multimodal, efficient, and intended to be adapted to real healthcare tasks. If your project is specifically **raw ECG** , switch to **ECGFounder** immediately. (Google Research)",
  "title": "Best latest baseline cardiology model available today"
}