Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Journal: Transplantation
PMID: 39042768

Abstract

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Authors

  • Madhumitha Rabindranath
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Maryam Naghibzadeh
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Xun Zhao
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Sandra Holdsworth
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Michael Brudno
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Aman Sidhu
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Mamatha Bhat
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.

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