ncbi/OcularChat-VQA

--- license: cc-by-nc-sa-4.0 --- # OcularChat Dataset To ensure clinical relevance and contextual accuracy during model training, simulated patient-physician dialogues were generated using key clinical variables extracted from the AREDS dataset. These variables included demographic characteristics (age, gender, diabetes status, and smoking history) and ophthalmic features (presence of advanced AMD, maximum drusen size, and presence of pigmentary abnormalities). # Datasets You can first download the [AREDS dataset](https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000001.v3.p1). Split it into train, valid, test subsets. To generate your own patient-physician dialogues, please run: ``` python genterate_areds.py ``` We also share our generated dialogues for your convenient research-only purpose. # Disclaimer This tool shows the results of research conducted in the Computational Biology Branch, DIR/NLM. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the information produced on this website, please see a health care professional. More information about NLM's disclaimer policy is available at https://www.nlm.nih.gov/web_policies.html. # Acknowledgement This research was supported by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. # Citation If you find our work helpful, pleaes cite it by: ```bibtex @article{gu2026ocularchat, title={Toward Multimodal Conversational AI for Age-Related Macular Degeneration}, author={Ran Gu, Benjamin Hou, Mélanie Hébert, Asmita Indurkar, Yifan Yang, Emily Y. Chew, Tiarnán D. L. Keenan, Zhiyong Lu}, year={2026} } ```许可证：CC-BY-NC-SA-4.0 # 眼科对话（OcularChat）数据集 为确保模型训练过程中的临床相关性与语境准确性，研究团队通过提取自年龄相关性眼病研究（AREDS）数据集的关键临床变量，生成了模拟医患对话文本。上述变量涵盖人口统计学特征（年龄、性别、糖尿病患病状态、吸烟史）以及眼科特征（晚期年龄相关性黄斑变性（AMD）患病情况、最大玻璃膜疣尺寸、色素异常发生情况）。 # 数据集 您可首先下载[年龄相关性眼病研究（AREDS）数据集](https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000001.v3.p1)，并将其划分为训练集、验证集与测试子集。 若需自行生成医患对话文本，请执行以下脚本： python genterate_areds.py 我们同时公开了已生成的对话文本，仅供非商业研究使用。 # 免责声明 本工具仅展示美国国立医学图书馆（NLM）计算生物学分部的研究成果。本网站生成的信息未经临床专业人员审核与监督，不得直接用于临床诊断或医疗决策。个人不应仅依据本网站生成的信息改变自身健康行为。美国国立卫生研究院（NIH）未独立验证本工具生成信息的有效性或实用性。若您对本网站生成的信息存在疑问，请咨询医疗保健专业人员。有关美国国立医学图书馆免责声明政策的更多信息，请访问https://www.nlm.nih.gov/web_policies.html。 # 致谢 本研究得到美国国立卫生研究院（NIH）院内研究计划的支持。本文的NIH作者贡献均属于美国政府作品。本文提出的研究结果与结论仅代表作者本人，未必反映美国国立卫生研究院或美国卫生与公众服务部的观点。 # 引用方式 若您认为本研究对您的工作有所帮助，请通过以下BibTeX格式引用： bibtex @article{gu2026ocularchat, title={Toward Multimodal Conversational AI for Age-Related Macular Degeneration}, author={Ran Gu, Benjamin Hou, Mélanie Hébert, Asmita Indurkar, Yifan Yang, Emily Y. Chew, Tiarnán D. L. Keenan, Zhiyong Lu}, year={2026} }