README.md

---
license: cc-by-4.0
language:
- en
- es
- fr
- it
tags:
- casimedicos
- explainability
- medical exams
- medical question answering
- multilinguality
- LLMs
- LLM
pretty_name: MedExpQA
configs:
- config_name: en
  data_files:
  - split: train
    path:
    - data/en/train.en.casimedicos.rag.jsonl
  - split: validation
    path:
    - data/en/dev.en.casimedicos.rag.jsonl
  - split: test
    path:
    - data/en/test.en.casimedicos.rag.jsonl
- config_name: es
  data_files:
  - split: train
    path:
    - data/es/train.es.casimedicos.rag.jsonl
  - split: validation
    path:
    - data/es/dev.es.casimedicos.rag.jsonl
  - split: test
    path:
    - data/es/test.es.casimedicos.rag.jsonl
- config_name: fr
  data_files:
  - split: train
    path:
    - data/fr/train.fr.casimedicos.rag.jsonl
  - split: validation
    path:
    - data/fr/dev.fr.casimedicos.rag.jsonl
  - split: test
    path:
    - data/fr/test.fr.casimedicos.rag.jsonl
- config_name: it
  data_files:
  - split: train
    path:
    - data/it/train.it.casimedicos.rag.jsonl
  - split: validation
    path:
    - data/it/dev.it.casimedicos.rag.jsonl
  - split: test
    path:
    - data/it/test.it.casimedicos.rag.jsonl
task_categories:
- text-generation
- question-answering
size_categories:
- 1K<n<10K
---

<p align="center">
    <br>
    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 200px;">
    <br>

# MexExpQA: Multilingual Benchmarking of Medical QA with reference gold explanations and Retrieval Augmented Generation (RAG)

We present a new multilingual parallel medical benchmark, MedExpQA, for the evaluation of LLMs on Medical Question Answering.
This benchmark can be used for various NLP tasks including: **Medical Question Answering** or **Explanation Generation**.

Although the design of MedExpQA is independent of any specific dataset, for the first version of the MedExpQA benchmark we leverage the commented MIR exams 
from the [Antidote CasiMedicos dataset which includes gold reference explanations](https://huggingface.co/datasets/HiTZ/casimedicos-exp), which is currently 
available for 4 languages: **English, French, Italian and Spanish**.

<table style="width:33%">
    <tr>
         <th>Antidote CasiMedicos splits</th>
     <tr>
         <td>train</td>
         <td>434</td>
     </tr>
     <tr>
         <td>validation</td>
         <td>63</td>
     </tr>
     <tr>
         <td>test</td>
         <td>125</td>
     </tr>
 </table>

- 📖 Paper:[MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering](https://doi.org/10.1016/j.artmed.2024.102938)
- 💻 Github Repo (Data and Code): [https://github.com/hitz-zentroa/MedExpQA](https://github.com/hitz-zentroa/MedExpQA)
- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
- Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR


## Example of Document in Antidote CasiMedicos Dataset

<p align="center">
<img src="https://github.com/ixa-ehu/antidote-casimedicos/blob/main/casimedicos-exp.png?raw=true" style="height: 600px;">
</p>

In this repository you can find the following data:

- **casimedicos-raw**: The textual content including Clinical Case (C), Question (Q), Possible Answers (P), and Explanation (E) as shown in the example above.
- **casimedicos-exp**: The manual annotations linking the explanations of the correct and incorrect possible answers.
- **MedExpQA**: benchmark for Medical QA based on gold reference explanations from casimedicos-exp and knowledge automatically extracted using RAG methods.

## Data Explanation

The following attributes composed **casimedicos-raw**:

- **id**: unique doc identifier.
- **year**: year in which the exam was published by the Spanish Ministry of Health.
- **question_id_specific**: id given to the original exam published by the Spanish Ministry of Health.
- **full_question**: Clinical Case (C) and Question (Q) as illustrated in the example document above.
- **full answer**: Full commented explanation (E) as illustrated in the example document above.
- **type**: medical speciality.
- **options**: Possible Answers (P) as illustrated in the example document above.
- **correct option**: solution to the exam question.

Additionally, the following jsonl attribute was added to create **casimedicos-exp**:

- **explanations**: for each possible answer above, manual annotation states whether:
  1. the explanation for each possible answer exists in the full comment (E) and
  2. if present, then we provide character and token offsets plus the text corresponding to the explanation for each possible answer.

For **MedExpQA** benchmarking we have added the following elements in the data:

- **rag**
  1. **clinical_case_options/MedCorp/RRF-2**: 32 snippets extracted from the MedCorp corpus using the combination of _clinical case_ and _options_ as a
  query during the retrieval process. These 32 snippets are the resulting RRF combination of 32 separately retrieved snippets using BM25 and MedCPT.


## MedExpQA Benchmark Overview

<p align="left">
<img src="https://github.com/hitz-zentroa/MedExpQA/blob/main/out/experiments/figures/overall_system.png?raw=true" style="height: 300px;">
</p>

## Prompt Example for LLMs

<p align="left">
<img src="https://github.com/hitz-zentroa/MedExpQA/blob/main/out/experiments/figures/prompt_en.png?raw=true" style="height: 250px;">
</p>

## Benchmark Results (averaged per type of external knowledge for grounding)

LLMs evaluated:  [LLaMA](https://huggingface.co/meta-llama/Llama-2-13b), [PMC-LLaMA](https://huggingface.co/axiong/PMC_LLaMA_13B), 
[Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) and [BioMistral](https://huggingface.co/BioMistral/BioMistral-7B-DARE).

<p align="left">
<img src="https://github.com/hitz-zentroa/MedExpQA/blob/main/out/experiments/figures/benchmark.png?raw=true" style="height: 300px;">
</p>


## Citation

If you use MedExpQA then please **cite the following paper**:

```bibtex
@article{ALONSO2024102938,
title = {MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question Answering},
journal = {Artificial Intelligence in Medicine},
pages = {102938},
year = {2024},
issn = {0933-3657},
doi = {https://doi.org/10.1016/j.artmed.2024.102938},
url = {https://www.sciencedirect.com/science/article/pii/S0933365724001805},
author = {Iñigo Alonso and Maite Oronoz and Rodrigo Agerri},
keywords = {Large Language Models, Medical Question Answering, Multilinguality, Retrieval Augmented Generation, Natural Language Processing},
}
```

**Contact**: [Iñigo Alonso](https://hitz.ehu.eus/en/node/282) and [Rodrigo Agerri](https://ragerri.github.io/)
HiTZ Center - Ixa, University of the Basque Country UPV/EHU