转化式学习
计算机科学
人工智能
领域
精密医学
比例(比率)
可靠性(半导体)
深度学习
数据科学
机器学习
医学
心理学
病理
物理
功率(物理)
法学
量子力学
教育学
政治学
作者
Nan Luo,Xiaojing Zhong,Luxin Su,Zilin Cheng,Wenyi Ma,Pingsheng Hao
标识
DOI:10.1016/j.compbiomed.2023.107413
摘要
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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