TY - JOUR
T1 - Invited Review for 20th Anniversary Special Issue of PLRev “AI for Mechanomedicine”
AU - Xie, Ning
AU - Tian, Jin
AU - Li, Zedong
AU - Shi, Nianyuan
AU - Li, Bin
AU - Cheng, Bo
AU - Li, Ye
AU - Li, Moxiao
AU - Xu, Feng
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/12
Y1 - 2024/12
N2 - Mechanomedicine is an interdisciplinary field that combines different areas including biomechanics, mechanobiology, and clinical applications like mechanodiagnosis and mechanotherapy. The emergence of artificial intelligence (AI) has revolutionized mechanomedicine, providing advanced tools to analyze the complex interactions between mechanics and biology. This review explores how AI impacts mechanomedicine across four key aspects, i.e., biomechanics, mechanobiology, mechanodiagnosis, and mechanotherapy. AI improves the accuracy of biomechanical characterizations and models, deepens the understanding of cellular mechanotransduction pathways, and enables early disease detection through mechanodiagnosis. In addition, AI optimizes mechanotherapy that targets biomechanical features and mechanobiological markers by personalizing treatment strategies based on real-time patient data. Even with these advancements, challenges still exist, particularly in data quality and the ethical integration into AI in clinical practice. The integration of AI with mechanomedicine offers transformative potential, enabling more accurate diagnostics and personalized treatments, and discovering novel mechanobiological pathways.
AB - Mechanomedicine is an interdisciplinary field that combines different areas including biomechanics, mechanobiology, and clinical applications like mechanodiagnosis and mechanotherapy. The emergence of artificial intelligence (AI) has revolutionized mechanomedicine, providing advanced tools to analyze the complex interactions between mechanics and biology. This review explores how AI impacts mechanomedicine across four key aspects, i.e., biomechanics, mechanobiology, mechanodiagnosis, and mechanotherapy. AI improves the accuracy of biomechanical characterizations and models, deepens the understanding of cellular mechanotransduction pathways, and enables early disease detection through mechanodiagnosis. In addition, AI optimizes mechanotherapy that targets biomechanical features and mechanobiological markers by personalizing treatment strategies based on real-time patient data. Even with these advancements, challenges still exist, particularly in data quality and the ethical integration into AI in clinical practice. The integration of AI with mechanomedicine offers transformative potential, enabling more accurate diagnostics and personalized treatments, and discovering novel mechanobiological pathways.
KW - Artificial Intelligence
KW - Biomechanics
KW - Mechanobiology
KW - Mechanodiagnosis
KW - Mechanomedicine
KW - Mechanotherapy
UR - https://www.scopus.com/pages/publications/85207882633
U2 - 10.1016/j.plrev.2024.10.010
DO - 10.1016/j.plrev.2024.10.010
M3 - 文献综述
C2 - 39489078
AN - SCOPUS:85207882633
SN - 1571-0645
VL - 51
SP - 328
EP - 342
JO - Physics of Life Reviews
JF - Physics of Life Reviews
ER -