MEDIQA-M3G@ ClinicalNLP 2024

MEDIQA-M3G @ NAACL-ClinicalNLP 2024

Multilingual & Multimodal Medical Answer Generation

Motivation

The rapid development of telecommunication technologies, the increased demands for healthcare services, and recent pandemic needs, have accelerated the adoption of remote clinical diagnosis and treatment. In addition to live meetings with doctors which may be conducted through telephone or video, asynchronous options such as e-visits, emails, and messaging chats have also been proven to be cost-effective and convenient.

In this task, we focus on the problem of clinical dermatology multimodal query response generation. Inputs will include text which give clinical context and queries, as well as one or more images. The challenge will tackle the generation an appropriate textual response to the query.

Consumer health question answering has been the subject of past challenges and research; however, these prior works only focus on text [1]. Previous work on visual question answering have focused mainly on radiology images and did not include additional clinical text input [2]. Also, while there is much work on dermatology image classification, much prior work is related to lesion malignancy classification for dermatoscope images[3].

To the best of our knowledge, this is the first challenge and study of a problem that seeks to automatically generate clinical responses, given textual clinical history, as well as user generated images and queries.

[1] Overview of the MEDIQA 2019 shared task on textual inference, question entailment and question answering. Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman. https://aclanthology.org/W19-5039/

[2] Vqa-med: Overview of the medical visual question answering task at imageclef 2019. Asma Ben Abacha , Sadid A. Hasan , Vivek V. Datla , Joey Liu , Dina Demner-Fushman , and Henning Muller. https://www.semanticscholar.org/paper/VQA-Med%3A-Overview-of-the-Medical-Visual-Question-at-Abacha-Hasan/9eeeb23546d3d2bbc73959bffc6819f2335f3c83

[3] Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. Zhouxiao Li, Konstantin Christoph Koban, Thilo Ludwig Schenck, Riccardo Enzo Giunta, Qingfeng Li, and Yangbai Sun. https://pubmed.ncbi.nlm.nih.gov/36431301/

Registration, Datasets & Evaluation 

• Please complete the ClinicalNLP-MEDIQA 2024 registration form first: https://docs.google.com/forms/d/e/1FAIpQLScUnP2TJQX996BR-6dd6GvWAmkfE8VrX135I3VAYuecD1VR9Q/viewform?vc=0&c=0&w=1&flr=0

• Accept the Terms and Conditions and join the Codabench project: https://www.codabench.org/competitions/1632/ 

Contact    

• If you have any questions regarding your team's registration, please email us at mediqa.organizers@gmail.com

• For more updates or inquiries, join the MEDIQA Google group https://groups.google.com/g/mediqa-nlp and email us at mediqa-nlp@googlegroups.com (mailing list)