🔥 Infherno

Infherno is an end-to-end agent that transforms unstructured clinical notes into structured FHIR (Fast Healthcare Interoperability Resources) format. It automates the parsing and mapping of free-text medical documentation into standardized FHIR resources, enabling interoperability across healthcare systems.

Built on Hugging Face’s SmolAgents library, Infherno supports multi-step reasoning, tool use, and modular extensibility for complex clinical information extraction.

Infherno also provides ontology support for SNOMED CT and HL7 ValueSets using Retrieval-Augmented Generation (RAG). This allows the agent to ground extracted medical concepts in standardized terminologies, ensuring semantic consistency and accurate coding in line with clinical data standards.

Live Demo

Gradio Demo Video: See the clip here.
Public Demo: A public demo is available at https://infherno.misit-augsburg.de/. It runs with an internal Snowstorm instance.

Our Gradio demo is accessible via Hugging Face Spaces. Although a Hugging Face Spaces instance was successfully running, we would like to ask you to use the other public demo above. Use of Local Models: For optimal performance and reliable results, it is recommended to use a strong commercial LLM like Gemini Pro 2.5, which was the model used in the experiments described in this paper. While local models were explored to a limited extent, their performance was observed to be substantially less reliable, and they were not used for the main evaluations. Also due to resource and context limitations with open-source models, we recommend launching Infherno locally with a proprietary model via API.

Run Infherno locally

Note: Expect most local models to yield substantially inferior results, as outlined in the previous section.

Install the dependencies first.

python3 -m venv env
source env/bin/activate

python3 -m pip install -r requirements.txt

Run the Infherno agent as follows:

# Define self-hosted Snowstorm instance
export SNOWSTORM_URL="http://<SNOMED-Instance>"

# Set Ollama endpoint
export OLLAMA_ENDPOINT="http://127.0.0.1:11434"

# Define custom open-weights model from Ollama to be used.
# MAKE SURE THAT THE MODEL IS ALREADY PULLED!
cat > local_config.py <<EOF
MODEL_ID = "ollama_chat/deepseek-r1:32b"
EOF

# Run the agent with dummy data
PYTHONPATH=. python3 infherno/smol_fhiragent.py

# Check the results in the logs:
cat logs/*.log

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Citing Infherno

Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes
Johann Frei, Nils Feldhus, Lisa Raithel, Roland Roller, Alexander Meyer, Frank Kramer
arXiv URL: https://arxiv.org/abs/2507.12261

@article{frei-2025-infherno,
    title = {Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes}, 
    author = {Johann Frei and Nils Feldhus and Lisa Raithel and Roland Roller and Alexander Meyer and Frank Kramer},
    year = {2025},
    volume = {abs/2507.12261},
    journal = {arXiv},
    primaryClass = {cs.CL},
    url = {https://arxiv.org/abs/2507.12261}, 
}