A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data

Yifan Wu, Yang Liu, Yue Yang, Michael S. Yao, Wenli Yang, Xuehui Shi, Lihong Yang, Dongjun Li, Yueming Liu, Shiyi Yin, Chunyan Lei, Meixia Zhang, James C. Gee, Xuan Yang (), Wenbin Wei () and Shi Gu ()
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Yifan Wu: University of Pennsylvania
Yang Liu: University of Electronic Science and Technology of China
Yue Yang: University of Pennsylvania
Michael S. Yao: University of Pennsylvania
Wenli Yang: Capital Medical University
Xuehui Shi: Capital Medical University
Lihong Yang: Capital Medical University
Dongjun Li: Capital Medical University
Yueming Liu: Capital Medical University
Shiyi Yin: Capital Medical University
Chunyan Lei: Sichuan University
Meixia Zhang: Sichuan University
James C. Gee: University of Pennsylvania
Xuan Yang: Capital Medical University
Wenbin Wei: Capital Medical University
Shi Gu: University of Electronic Science and Technology of China

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.

Date: 2025
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DOI: 10.1038/s41467-025-58801-7

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