Artificial Intelligence in Lymphoma Histopathology: Systematic Review.

Journal: Journal of medical Internet research
PMID:

Abstract

BACKGROUND: Artificial intelligence (AI) shows considerable promise in the areas of lymphoma diagnosis, prognosis, and gene prediction. However, a comprehensive assessment of potential biases and the clinical utility of AI models is still needed.

Authors

  • Yao Fu
    Hefei National Laboratory for Physical Sciences at the Microscale, CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Center for Excellence in Molecular Synthesis of CAS, Institute of Energy, Hefei Comprehensive National Science Center, University of Science and Technology of China, Hefei 230026, China.
  • Zongyao Huang
    Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Xudong Deng
    College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410200, China.
  • Linna Xu
    Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Mingxing Zhang
    Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Jinyi Liu
    Phase I Clinical Trial Unit, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Bin Huang
    Department of Clinical Laboratory, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.