Hi @codeautomation , and welcome to the HAI-DEF community! Thank you for kicking off such an important discussion. Below, we outline some of the HAI-DEF team’s efforts in evaluating model performance, resources and tutorials that we’ve made available, and challenges we’ve observed in the community when adapting open models for healthcare applications.
1. Evaluating Health AI Model Performance
Automated metrics often fall short in capturing the nuances of clinical text, but they remain foundational. While models are evaluated across standard clinical benchmarks (such as MedQA, MedMCQA, MIMIC-CXR, and RAD_VQA), we track specific quantitative metrics depending on the clinical task:
Accuracy & Macro F1: Used primarily for medical question answering and multimodal multiple-choice questions (e.g., MedQA, MedMCQA).
ROUGE-L: Utilized for open-ended Visual Question Answering (VQA) to measure the alignment between generated answers and reference text.
RadGraph F1: For complex generative tasks like Chest X-ray report generation (e.g., MIMIC-CXR), we rely on the RadGraph F1 metric. This evaluates the clinical accuracy of the generated findings and entities, which is far more reliable than simple text-overlap metrics.
Note on other medical data modalities: As models expand to handle new capabilities, evaluation metrics must adapt. For spatial awareness tasks like anatomical localization (generating bounding boxes), we rely on Intersection over Union (IoU). For structured document understanding, such as extracting key-value pairs from laboratory report PDFs, we utilize granular Precision, Recall, and F1 scores. Finally, for general visual question answering on medical images, Tokenized F1 is frequently evaluated.
However, because metrics have limits, it is critical to maintain a strong focus on expert human evaluations for specific clinical tasks.
Here are the references:[MedGemma 1.5 Technical] Report( [2604.05081] MedGemma 1.5 Technical Report ), MedGemma 1.5 Model Card
2. Balancing Accuracy with Safety and Reliability
In healthcare, safety must be maintained through architectural boundaries. As outlined in the MedGemma Model Card, the use of these models is governed by the Health AI Developer Foundations terms of use. Under these principles, foundational models require validation for specific use cases and are explicitly not intended to autonomously diagnose or suggest treatments for any disease or condition.
Reference: MedGemma Model Card & Intended Use
3. Challenges When Adapting Open Models
Quantization and Formatting Precision: To run models locally on constrained hardware, developers often use quantization. However, uniform quantization (like Q8_0) on smaller models can introduce slight numerical drift over long sequences. This can lead to precision loss on structural syntax tokens (like curly braces or commas), degrading JSON schema adherence even when the clinical reasoning remains correct. Enforcing strict JSON schemas at the inference engine level is a common mitigation strategy.
Handling Complex Modalities: Healthcare requires specialized formats. Adapting models requires robust preprocessing capabilities to handle high-dimensional imaging like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Whole-slide histopathology imaging (WSI). Please refer notebooks
We hope this provides some helpful context on how these challenges are approached.
Regarding learnings from real world deployments, do check out our HAI-DEF Showcase. We also encourage our community to comment and share their learnings in this space.
Thank You