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Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Updated: May 6, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

深度学习的发展医疗图像分析和解释的人工神经网络技术

Olamilekan Shobayo^1,2, Reza Saatchi¹

¹School of Engineering and Built Environment, Sheffield Hallam University, Pond Street, Sheffield S1 1WB, UK.

Diagnostics (Basel, Switzerland)

|May 14, 2025

概括

深度学习显著增强了用于诊断的医学图像分析. 关键的挑战包括数据,可解释性和伦理学,但进展承诺改善患者的治疗结果.

关键词:

人工智能的人工智能是人工智能.人工神经网络的人工神经网络深度学习是一种深度学习.图像分类和模式识别.医疗图像分析分析

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科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
诊断技术诊断技术的使用

背景情况:

深度学习 (DL) 彻底改变了医学图像分析,提供自动化,高效和准确的诊断解决方案.
医疗成像技术的进步正在改变医疗成像数据在各种模式的解释.

研究的目的:

探索医学成像深度学习技术的最新发展.
确定临床采用的关键挑战和未来研究方向.

主要方法:

使用系统性审查和元分析 (PRISMA) 准则的首选报告项目进行系统性文献审查.
在PubMed,谷歌学者和Scopus数据库中进行的搜索.
探索各种DL架构,包括CNN,RNN,GAN,U-Nets,ViT和混合模型.

主要成果:

深度学习模型在提高MRI,CT,US和X射线的诊断准确度方面显示出显著的潜力.
确定的主要挑战包括数据可用性,可解释性,过拟合性,计算需求,模型信任,数据隐私和道德考虑.
各种DL架构对于分类,细分,特征提取和图像合成都是有效的.

结论:

深度学习为改善医学成像诊断准确性提供了巨大的希望.
解决数据,可解释性,伦理和计算效率方面的挑战对于更广泛的临床采用至关重要.
未来的研究应该专注于实时应用,增强可解释性,以及整合到医疗保健框架中,以获得更好的患者结果.