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November 28, 2025IEEE Journal of Biomedical and Health InformaticsOpen Access

XFM: An Explainable Knowledge-Fused Vision Foundation Model for Improving Clinical Diagnosis of Low-Prevalence Retinal Diseases

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Clinical diagnosis improved accuracy through targeted retention of prior knowledge, enhancing ophthalmology practices.
Evaluation on two public datasets resulted in significant improvements, including a 9% accuracy increase with few-shot learning techniques.
Adoption of explainable models boosts generalization capabilities of algorithms in low-data environments for medical applications.
Findings support the integration of advanced techniques in AI-driven clinical solutions for better retinal disease identification.

Authors

HJHongyang Jiang MGMengdi Gao BXBowen Xu

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Implication

Analysis demonstrates improved clinical diagnosis of low-prevalence retinal diseases using explainable foundation models, indicating enhanced transfer learning and infusion techniques.

Cite This Study

Jiang et al. (2025) studied this question.

synapsesocial.com/papers/6928f12ea65b730b9ea7a74c https://doi.org/10.1109/jbhi.2025.3624305

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Implication

Analysis demonstrates improved clinical diagnosis of low-prevalence retinal diseases using explainable foundation models, indicating enhanced transfer learning and infusion techniques.

XFM: An Explainable Knowledge-Fused Vision Foundation Model for Improving Clinical Diagnosis of Low-Prevalence Retinal Diseases

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Discussion

Implication

Cite This Study

XFM: An Explainable Knowledge-Fused Vision Foundation Model for Improving Clinical Diagnosis of Low-Prevalence Retinal Diseases

Key Points

Discussion

Implication

Cite This Study

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