---
title: "BDI at MEDIQA-EVAL 2026 — paper by Xu et al. (2026)"
description: "The team builds a ReAct-style agent on an off-the-shelf LLM. 43 references, cited by 1 paper here."
authors: ["Justin Xu", "Zizheng Zhang", "Augustine Luk", "Benjamin Khong", "Haochen Cui", "Samuel Hwang", "Alyssa Pradhan", "Kevin Yuan", "David W. Eyre"]
year: 2026
venue: "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026"
source: "https://aclanthology.org/2026.clinicalnlp-1.16/"
doi: "10.63317/3xgwaboh563b"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-bdi-mediqa-eval-2026-react/"
---
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# BDI at MEDIQA-EVAL 2026: A ReAct-Style Multimodal Agent for Fine-Grained Medical Response Assessment

**Authors:** Justin Xu, [Zizheng Zhang](https://md.research.elamur.ai/free/en/authors/zizheng-zhang/), Augustine Luk, Benjamin Khong, Haochen Cui, Samuel Hwang, Alyssa Pradhan, Kevin Yuan and David W. Eyre · **Year:** 2026 · **Venue:** [Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/clinicalnlp/) ([Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/clinicalnlp/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.clinicalnlp-1.16/) · [PDF](https://aclanthology.org/2026.clinicalnlp-1.16.pdf) · [DOI 10.63317/3xgwaboh563b](https://doi.org/10.63317/3xgwaboh563b) · [OpenAlex](https://openalex.org/W7166558251)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Scoring free-text answers to patient questions that come with clinical photos is difficult. Several answers can be clinically valid, images must be interpreted, and overlap metrics such as BLEU or ROUGE match expert opinion poorly. Team BDI's entry to the MEDIQA-EVAL 2026 shared task (LREC 2026 ClinicalNLP Workshop) addresses this without pre-training or fine-tuning any foundation model. The team builds a ReAct-style agent on an off-the-shelf LLM. It breaks grading into three steps: collecting evidence, modelling structured features and reasoning toward a final score. Its toolbox includes:
- rubric-guided prompts
- retrieval of similar multimodal encounters
- explainable feature-based regression models that supply numeric priors
- VLM-generated visual QA references
- optional image augmentation

The agent rates candidate answers for dermatology and wound care cases in English and Chinese and is compared against baseline prompting methods. The authors argue that this modular, inference-time design is a workable and more auditable alternative to one-shot LLM-as-a-judge scoring. The supplied excerpt omits the results section, so no scores are reported here.

## Key points
- **Data:** DermaVQA (iiyi) has 56 dev and 100 test encounters. WoundcareVQA has 105 dev and 93 test encounters. Each encounter comes with three system answers and at least one clinician answer, in both EN and ZH.
- **English rubric:** five axes (Completeness, Factual Accuracy, Relevance, Writing Style, Overall Quality), each scored as {0, 0.5, 1}, plus a binary disagreement flag.
- **Chinese rubric:** only two criteria, factual consistency with human answers and writing quality, with no disagreement flag.
- **Evaluation:** Kendall's τ, Pearson's r and Spearman's ρ against expert ratings are averaged per metric and then across metrics, giving one leaderboard score per language.
- **Method:** the system is training-free. It combines a ReAct agent with retrieval, regression priors, VLM references and image tools to produce interpretable scores.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) — University, Berkeley, United States · 175 works · 227,440 citations
- [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) — University, Oxford, United Kingdom · 104 works · 157,183 citations
- [University of California, Los Angeles](https://md.research.elamur.ai/free/en/universities/university-of-california-los-angeles/) — University, Los Angeles, United States · 87 works · 54,961 citations

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## Cite

Cite as: Justin Xu, Zizheng Zhang, Augustine Luk, Benjamin Khong, Haochen Cui, Samuel Hwang, Alyssa Pradhan, Kevin Yuan and David W. Eyre. 2026. BDI at MEDIQA-EVAL 2026: A ReAct-Style Multimodal Agent for Fine-Grained Medical Response Assessment. Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026bdi,
  title = {{BDI at MEDIQA-EVAL 2026: A ReAct-Style Multimodal Agent for Fine-Grained Medical Response Assessment}},
  author = {Justin Xu, Zizheng Zhang, Augustine Luk, Benjamin Khong, Haochen Cui, Samuel Hwang, Alyssa Pradhan, Kevin Yuan and David W. Eyre},
  booktitle = {Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026},
  year = {2026},
  doi = {10.63317/3xgwaboh563b},
  url = {https://doi.org/10.63317/3xgwaboh563b}
}
```

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36. Wen-wai Yim, Asma Ben Abacha, Yujuan Fu, Zhaoyi Sun, Fei Xia, Meliha Yetisgen, and Martin Krallinger. 2024b. Overview of the MEDIQA-m3g 2024 shared task on multilingual multimodal medical answer generation. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 581–589. Assoc… [find](https://scholar.google.com/scholar?q=Wen%2Dwai+Yim%2C+Asma+Ben+Abacha%2C+Yujuan+Fu%2C+Zhaoyi+Sun%2C+Fei+Xia%2C+Meliha+Yetisgen%2C+and+Martin+Krallinger%2E+2024b%2E+Overview+of+the+MEDIQA%2Dm3g+2024+shared+task+on+multilingual+multimodal+medical+answer+gener)
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*Updated 31 October 2026*