Abstract
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.
Highlights
After decades of pioneering advances and improvements, kidney transplantation is the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD) because it offers higher survival rates and arguably better quality of life after transplantation
Kidney transplant databases should be one of the first widespread worldwide implementations of databases to take advantage of the growing field of Artificial intelligence (AI) in medicine, as the availability of more and more diverse datasets will enable better AI model generation, reducing biases derived from limited populations, without restricting findings that may be particular to one population
Many challenges plague this adoption as a standard, and future research will require broad inter-disciplinary initiatives to take full advantage of AI in the kidney transplantation field
Summary
After decades of pioneering advances and improvements, kidney transplantation is the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD) because it offers higher survival rates and arguably better quality of life after transplantation Despite this success, the high risk of premature death and frequent occurrence of graft failure requiring return to dialysis or re-transplantation remain important challenges for the research community and a constantly perceived threat for kidney transplant recipients (KTR) [1,2]. The clinical and research community is observ119 ing a burst of studies and other innovative initiatives using AI for a wide range of analytical and practical applications in biomedical areas This trend holds true in the kidney transplant field, which is evident from the number studies using AI techniques for the transplantof field, which is evident from the number studies using AI techniques for the prediction kidney transplant outcomes that have been published over the last decades, prediction kidney transplant that have been(Figure published but mostly of over the last.
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