Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual question answering (VQA) datasets that capture clinical reasoning and explicit image-text alignm...
Ling-Xuan Hou, Yu-Hua Xie, Yue Hu et al.· 0 citations
A FOundational LLM Trained on ThoughtMed-1M (FOLTMed), a scalable paradigm for advancing research on clinically grounded multimodal LLMs, achieved state-of-the-art performance across 42 medical VQA benchmark datasets, with a macro accuracy of 85.4%, and generated more clinically coherent responses on the ThoughtMed-1M...
Ling-Xuan Hou, Yu-Hua Xie, Yue Hu et al.· 0 citations
Experiments on loan approval datasets show that RASF improves accuracy by 3–5% over standard LCS while maintaining interpretable, rule-based outputs, highlighting the potential of RASF-LCS for explainable credit decision-making.
Ahamed Zahvie, Abubakar Siddique, Trung Nguyen et al.· GECCO Companion· 1 citation
A novel Ranked Attribute Selection with Midpoint Filtering with Midpoint Filtering (RASF) framework that extends LCS (EXTRACS) to enhance feature selection and rule validation for credit approval and supports explainable AI in credit scoring.
M. Ahamed, Abubakar Siddique, Trung Nguyen et al.· Proceedings of the Genetic a...· 0 citations
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