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Showing 1–9 of 9 results
Advanced filters: Author: Juexiao Zhou Clear advanced filters

  • Here, authors develop SkinGPT-4, an interactive dermatology diagnostic system that uses multimodal large language models and aligns a vision transformer with Llama-2-13b-chat. Evaluated by dermatologists, it offers autonomous diagnosis and treatment recommendations.

    • Juexiao Zhou
    • Xiaonan He
    • Xin Gao
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-12

  • Pulmonary artery-vein segmentation is essential for disease diagnosis but has not been used with non-contrast CT. Here the authors developed HiPaS, a deep learning method which enables this with no inferiority to CTPA performance and a large-scale anatomical study using HiPaS reveals pulmonary vessel differences associated with sex, age, and disease.

    • Yuetan Chu
    • Gongning Luo
    • Xin Gao
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-14

  • This study evaluates bias in AI-generated medical text, revealing disparities across race, sex and age. An optimization method is proposed to enhance fairness without compromising performance, offering a step toward more equitable AI in healthcare.

    • Xiuying Chen
    • Tairan Wang
    • Xiangliang Zhang
    Research
    Nature Computational Science
    Volume: 5, P: 388-396

  • Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.

    • Longxi Zhou
    • Xianglin Meng
    • Xin Gao
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 4, P: 494-503

  • SARS-CoV-2’s rapid evolution threatens public health. Here, authors present a deep learning approach to forecast high-risk mutations that may appear in the future, aiding vaccine development and enhancing preparedness against future variants.

    • Wenkai Han
    • Ningning Chen
    • Xin Gao
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-14

  • Question Answering (QA) models have emerged as crucial tools for acquiring knowledge and evaluating domain-specific abilities, however, the domain of chemical QA remains underexplored. Here, the authors report ScholarChemQA as a large-scale QA dataset and introduce a ChemMatch model for effectively answering chemical questions and acquiring chemical-related knowledge.

    • Xiuying Chen
    • Tairan Wang
    • Xiangliang Zhang
    ResearchOpen Access
    Communications Chemistry
    Volume: 8, P: 1-11