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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.
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通过人工智能推进生物材料研究.

Jun Chen Ng1, Pauline Shan Qing Yeoh2, Farina Muhamad2

  • 1Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; School of Materials Science and Engineering, Peking University, Beijing, China.

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概括

人工智能 (AI) 通过优化开发和克服制造挑战来加速生物材料创新. 本综述探讨了人工智能应用,方法和发展医疗保健生物材料的局限性.

关键词:
人工智能的人工智能是人工智能.陶生物材料是一种陶生物材料.复合生物材料是一种复合生物材料.可解释的人工智能机器学习 机器学习金属生物材料金属生物材料聚合物生物材料的聚合物生物材料.

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科学领域:

  • 生物材料科学与工程 生物材料科学与工程
  • 计算材料科学科学 计算材料科学
  • 医疗器械开发 医疗器械开发

背景情况:

  • 生物材料在医疗保健中至关重要,特别是在植入物中,需要预防不良影响.
  • 传统的生物材料开发是耗时的,劳动密集的和昂贵的.
  • 人工智能 (AI) 为加速生物材料研究和创新提供了一种变革性的方法.

研究的目的:

  • 为生物材料研究中人工智能应用提供全面的审查.
  • 检查机器学习 (ML) 和深度学习 (DL) 在各种生物材料类别中的作用.
  • 讨论人工智能方法及其对生物材料设计和表征的影响.

主要方法:

  • 对生物材料中人工智能应用现有文献的审查.
  • 人工智能方法的分类 (监督,无监督,半监督,强化学习).
  • 对人工智能在生物材料前进和反向设计问题中的作用的分析.

主要成果:

  • 包括ML和DL在内的AI显著提高了生物材料开发的性能,效率和可扩展性.
  • 人工智能解决了聚合物,金属,陶和复合生物材料的制造和表征方面的挑战.
  • 确定了可解释性和数据质量等关键的人工智能局限性,并讨论了可解释性AI (XAI) 等新兴解决方案.

结论:

  • 人工智能是一个强大的工具,可以彻底改变生物材料的研发.
  • 可解释的AI方法 (SHAP,LIME) 对于解决生物材料应用中的AI局限性至关重要.
  • 人工智能的整合有望在为医疗保健创造下一代生物材料方面取得重大进展.