MedVAL: Toward Expert-Level Medical Text Validation with Language Models

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.

🏥 What is MedVAL?

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.

⚡️ Installation

Environment Setup

Create and activate the conda environment:

conda env create -f env.yml
conda activate medval

🚀 Evaluation Instructions

python run.py --config=test

1. API-based Models

For evaluating API-based models (OpenAI, Anthropic, Gemini, etc.):

Configuration (configs/test.yaml):

tasks: [dialogue2note, medication2answer, query2question, report2impression]
data: test
method: zero-shot # [zero-shot, finetune]

n_samples: null
debug: False
input_csv: null  # Optional: Path to custom CSV file

model: openai/gpt-4o-mini
api_base: null
api_key: ${API_KEY}
local_model_path: null

2. Local/Huggingface Models

For evaluating local or HuggingFace models:

Configuration (configs/test.yaml):

tasks: [dialogue2note, medication2answer, query2question, report2impression]
data: test
method: zero-shot # [zero-shot, finetune]

n_samples: null
debug: False
input_csv: null  # Optional: Path to custom CSV file

model: local/MODEL_NAME
api_base: null
api_key: null
local_model_path: /path/to/local/model

🔥 Fine-Tuning Instructions

python run.py --config=train

1. API-based Teacher Models

For fine-tuning a local student model using an API-based teacher model:

Configuration (configs/train.yaml):

tasks: [medication2answer, query2question, report2impression, report2simplified]
data: train
method: finetune

n_samples: null
debug: False
num_threads: 16
num_epochs: 5
threshold: 0.95

model: openai/gpt-4o-mini
api_base: null
api_key: ${API_KEY}

student_model: local/STUDENT_MODEL_NAME
local_model_path: /path/to/student/model

2. Local/Huggingface Models

For fine-tuning a local student model using a local teacher model:

Configuration (configs/train.yaml):

tasks: [medication2answer, query2question, report2impression, report2simplified]
data: train
method: finetune

n_samples: null
debug: False
num_threads: 16
num_epochs: 5
threshold: 0.95

model: local/MODEL_NAME
api_base: null
api_key: null

student_model: local/MODEL_NAME
local_model_path: /path/to/local/model

🔧 API Model Configurations

OpenAI

model: openai/MODEL_NAME
api_base: null
api_key: ${OPENAI_API_KEY}

Gemini

model: gemini/MODEL_NAME
api_base: null
api_key: ${GEMINI_API_KEY}

Anthropic

model: anthropic/MODEL_NAME
api_base: null
api_key: ${ANTHROPIC_API_KEY}

SGLang

model: openai/HUGGINGFACE_MODEL_NAME
api_base: http://SERVER_IP:PORT/v1
api_key: local

Ollama

model: ollama_chat/MODEL_NAME
api_base: http://SERVER_IP:PORT
api_key: null

📊 Dataset and Fine-Tuned Model

1. Dataset Loading:

   • By default, the MedVAL-Bench dataset is automatically loaded from HuggingFace: load_dataset("stanfordmimi/MedVAL-Bench").
   • 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).

2. MedVAL-4B Model

   • 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.

🎯 Configuration Parameters

Core Parameters

• tasks: List of tasks for fine-tuning/evaluation
• data: Dataset split (train or test)
• method: Evaluation method (zero-shot or finetune)
• n_samples: Number of samples to process (null for all)
• debug: Enable debug mode for detailed output

Model Parameters

• model: Model identifier (API or local)
• api_base: API endpoint URL
• api_key: API key (use ${ENV_VAR} for environment variables)
• local_model_path: Path to local model files

Fine-tuning Parameters

• student_model: Student model for fine-tuning
• num_threads: Number of threads for training
• num_epochs: Training epochs
• threshold: Filtering threshold

📈 Results

Results are automatically saved to the results/ directory with the following structure:

results/
├── zero-shot/
│   └── model_name/
│       └── dataset_name.csv
└── finetune/
    └── model_name/
        └── dataset_name.csv

🏗️ Project Structure

MedVAL/
├── configs/          # Configuration files
├── medval/           # Core package
│   ├── pipeline.py   # Main MedVAL pipeline
│   ├── generator.py  # Text generation module
│   └── validator.py  # Validation module
├── utils/            # Utility functions and prompts
├── agents/           # Fine-tuned model storage
├── results/          # Evaluation results
└── run.py            # Main execution script

🤝 Contributing

We welcome contributions to improve MedVAL! Please feel free to submit issues, feature requests, or pull requests.

🙏 Acknowledgments

This repository is built using DSPy for language model fine-tuning/evaluation.

📎 Citation

If you find this repository useful for your work, please cite the following paper:

@article{aali2025medval,
  title={MedVAL: Toward Expert-Level Medical Text Validation with Language Models},
  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},
  journal={arXiv preprint arXiv:2507.03152},
  year={2025}
}