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揭示人工智能在变革疾病诊断与预测方面的潜力:机器学习和深度学习方法的全面综述

Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.

作者信息

Sadr Hossein, Nazari Mojdeh, Khodaverdian Zeinab, Farzan Ramyar, Yousefzadeh-Chabok Shahrokh, Ashoobi Mohammad Taghi, Hemmati Hossein, Hendi Amirreza, Ashraf Ali, Pedram Mir Mohsen, Hasannejad-Bibalan Meysam, Yamaghani Mohammad Reza

机构信息

Department of Artificial Intelligence in Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.

Neuroscience Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran.

出版信息

Eur J Med Res. 2025 May 26;30(1):418. doi: 10.1186/s40001-025-02680-7.

Abstract

The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized healthcare, particularly in the domains of disease prediction and diagnosis. This study provides a comprehensive review of ML and DL applications across sixteen diverse diseases, synthesizing findings from research conducted between 2015 and 2024. We explore these technologies' methodologies, effectiveness, and clinical outcomes, highlighting their transformative potential in healthcare settings. Although ML and DL demonstrate remarkable accuracy and efficiency in disease prediction and diagnosis, challenges including quality of data, interpretability of models, and their integration into clinical workflows remain significant barriers. By evaluating advanced approaches and their outcomes, this review not only underscores the current capabilities of ML and DL but also identifies key areas for future research. Ultimately, this work aims to serve as a roadmap for advancing healthcare practices, enhancing clinical decision making, and strengthening patient outcomes through the effective and responsible implementation of AI-driven technologies.

摘要

机器学习(ML)和深度学习(DL)技术的快速发展彻底改变了医疗保健领域,尤其是在疾病预测和诊断方面。本研究全面回顾了ML和DL在16种不同疾病中的应用,综合了2015年至2024年期间开展的研究结果。我们探讨了这些技术的方法、有效性和临床结果,突出了它们在医疗环境中的变革潜力。尽管ML和DL在疾病预测和诊断中显示出显著的准确性和效率,但包括数据质量、模型可解释性以及它们融入临床工作流程等挑战仍然是重大障碍。通过评估先进方法及其结果,本综述不仅强调了ML和DL的当前能力,还确定了未来研究的关键领域。最终,这项工作旨在成为推进医疗实践、加强临床决策以及通过有效且负责任地实施人工智能驱动技术来改善患者预后的路线图。

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