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# MedVAL-pipeline
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# MedVAL: Toward Expert-Level Medical Text Validation with Language Models
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[](https://arxiv.org/abs/2507.03152) [](https://huggingface.co/stanfordmimi/MedVAL-4B) [](https://huggingface.co/datasets/stanfordmimi/MedVAL-Bench/) [](LICENSE)
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**Figure 1** | **MedVAL test-time workflow**. A generator LM produces an output, and MedVAL assesses the output's factual consistency with the input, while assigning a risk grade and determining its safety for deployment.
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## 🏥 What is MedVAL?
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MedVAL is a self-supervised framework for expert-level validation of AI-generated medical text using language models. The system is designed to evaluate the accuracy and safety of AI-generated medical text across multiple medical tasks. The framework supports both model fine-tuning and evaluation.
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## ⚡️ Installation
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### Environment Setup
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Create and activate the conda environment:
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Remove `numactl` from `env.yml` if you are using MacOS.
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```bash
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conda env create -f env.yml
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conda activate medval
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```
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## 🚀 Evaluation Instructions
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```bash
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python run.py --config=test
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```
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### 1. API-based Models
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For evaluating API-based models (OpenAI, Anthropic, Gemini, etc.):
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**Configuration (`configs/test.yaml`)**:
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```yaml
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tasks: [dialogue2note, medication2answer, query2question, report2impression]
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data: test
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method: zero-shot # [zero-shot, finetune]
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n_samples: null
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debug: False
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input_csv: null # Optional: Path to custom CSV file
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model: openai/gpt-4o-mini
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api_base: null
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api_key: ${API_KEY}
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local_model_path: null
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```
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### 2. Local/Huggingface Models
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For evaluating local or HuggingFace models:
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**Configuration (`configs/test.yaml`)**:
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```yaml
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tasks: [dialogue2note, medication2answer, query2question, report2impression]
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data: test
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method: zero-shot # [zero-shot, finetune]
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n_samples: null
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debug: False
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input_csv: null # Optional: Path to custom CSV file
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model: local/MODEL_NAME
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api_base: null
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api_key: null
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local_model_path: /path/to/local/model
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```
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## 🔥 Fine-Tuning Instructions
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```bash
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python run.py --config=train
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```
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### 1. API-based Teacher Models
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For fine-tuning a local student model using an API-based teacher model:
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**Configuration (`configs/train.yaml`)**:
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```yaml
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tasks: [medication2answer, query2question, report2impression, report2simplified]
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data: train
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method: finetune
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n_samples: null
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debug: False
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num_threads: 16
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num_epochs: 5
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threshold: 0.95
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model: openai/gpt-4o-mini
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api_base: null
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api_key: ${API_KEY}
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student_model: local/STUDENT_MODEL_NAME
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local_model_path: /path/to/student/model
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```
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### 2. Local/Huggingface Models
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For fine-tuning a local student model using a local teacher model:
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**Configuration (`configs/train.yaml`)**:
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```yaml
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tasks: [medication2answer, query2question, report2impression, report2simplified]
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data: train
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method: finetune
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n_samples: null
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debug: False
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num_threads: 16
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num_epochs: 5
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threshold: 0.95
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model: local/MODEL_NAME
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api_base: null
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api_key: null
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student_model: local/MODEL_NAME
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local_model_path: /path/to/local/model
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```
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## 🔧 API Model Configurations
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### OpenAI
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```yaml
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model: openai/MODEL_NAME
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api_base: null
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api_key: ${OPENAI_API_KEY}
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```
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### Gemini
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```yaml
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model: gemini/MODEL_NAME
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api_base: null
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api_key: ${GEMINI_API_KEY}
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```
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### Anthropic
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```yaml
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model: anthropic/MODEL_NAME
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api_base: null
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api_key: ${ANTHROPIC_API_KEY}
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```
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### SGLang
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```yaml
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model: openai/HUGGINGFACE_MODEL_NAME
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api_base: http://SERVER_IP:PORT/v1
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api_key: local
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```
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### Ollama
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```yaml
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model: ollama_chat/MODEL_NAME
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api_base: http://SERVER_IP:PORT
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api_key: null
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```
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## 📊 Dataset and Fine-Tuned Model
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1. **Dataset Loading:**
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- By default, the MedVAL-Bench dataset is automatically loaded from HuggingFace: ```load_dataset("stanfordmimi/MedVAL-Bench")```.
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- To use a custom CSV file, specify path in `configs/test.yaml`: ```input_csv: /path/to/csv``` (ensure custom CSV has similar column structure to the HuggingFace dataset).
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2. **MedVAL-4B Model**
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- MedVAL-4B can be downloaded from HuggingFace (```stanfordmimi/MedVAL-4B```). Once downloaded, run evaluation with MedVAL-4B by setting ```local_model_path: /path/to/medval-4b``` in the config.
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## 🎯 Configuration Parameters
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### Core Parameters
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- `tasks`: List of tasks for fine-tuning/evaluation
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- `data`: Dataset split (`train` or `test`)
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- `method`: Evaluation method (`zero-shot` or `finetune`)
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- `n_samples`: Number of samples to process (null for all)
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- `debug`: Enable debug mode for detailed output
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### Model Parameters
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- `model`: Model identifier (API or local)
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- `api_base`: API endpoint URL
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- `api_key`: API key (use `${ENV_VAR}` for environment variables)
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- `local_model_path`: Path to local model files
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### Fine-tuning Parameters
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- `student_model`: Student model for fine-tuning
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- `num_threads`: Number of threads for training
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- `num_epochs`: Training epochs
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- `threshold`: Filtering threshold
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## 📈 Results
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Results are automatically saved to the `results/` directory with the following structure:
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```
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results/
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├── zero-shot/
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│ └── model_name/
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│ └── dataset_name.csv
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└── finetune/
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└── model_name/
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└── dataset_name.csv
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```
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## 🏗️ Project Structure
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```
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MedVAL/
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├── configs/ # Configuration files
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├── medval/ # Core package
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│ ├── pipeline.py # Main MedVAL pipeline
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│ ├── generator.py # Text generation module
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│ └── validator.py # Validation module
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├── utils/ # Utility functions and prompts
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├── agents/ # Fine-tuned model storage
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├── results/ # Evaluation results
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└── run.py # Main execution script
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```
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## 🤝 Contributing
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We welcome contributions to improve MedVAL! Please feel free to submit issues, feature requests, or pull requests.
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## 🙏 Acknowledgments
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This repository is built using [DSPy](https://github.com/stanfordnlp/dspy) for language model fine-tuning/evaluation.
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## 📎 Citation
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If you find this repository useful for your work, please cite the following paper:
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```bibtex
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@article{aali2025medval,
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title={MedVAL: Toward Expert-Level Medical Text Validation with Language Models},
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author={Asad Aali and Vasiliki Bikia and Maya Varma and Nicole Chiou and Sophie Ostmeier and Arnav Singhvi and Magdalini Paschali and Ashwin Kumar and Andrew Johnston and Karimar Amador-Martinez and Eduardo Juan Perez Guerrero and Paola Naovi Cruz Rivera and Sergios Gatidis and Christian Bluethgen and Eduardo Pontes Reis and Eddy D. Zandee van Rilland and Poonam Laxmappa Hosamani and Kevin R Keet and Minjoung Go and Evelyn Ling and David B. Larson and Curtis Langlotz and Roxana Daneshjou and Jason Hom and Sanmi Koyejo and Emily Alsentzer and Akshay S. Chaudhari},
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journal={arXiv preprint arXiv:2507.03152},
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year={2025}
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}
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```
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