Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation
Architecture of an artificial neural network (ANN) for predicting kidney graft survival: a deep neural network with one input layer, two hidden layers, and one output layer. In this network, each neuron (N) of a layer is connected to neurons in the next one, yielding a fully connected network.
With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques.
Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide.
In this recent paper by researchers from Tunisia, the group provides an overview of AI from a medical perspective. They cover the following points:
- The core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started
- The technical challenges
- The unmet needs and the potential role that AI can play to fill the gaps in the KT field,
- The published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.
In summary, the areas of application of AI in the world are expanding exponentially. Nephrologists will have to interact with AI in their daily practice in the near future; however, the nephrology community needs to be well-informed regarding this technology. AI has the potential to help them reach the unmet needs in the field by enabling accurate predictions and data analysis of the use of conventional statistics, especially in this era of data abundance, by capturing complex relationships among large datasets with a large number of variables. With the existing KT databases and registries, AI technologies seem to be the best solution to meet current gaps, especially long-term outcomes. In order to generalize the use of AI in nephrology, nephrologists worldwide are required to understand the core concepts of AI and its subtypes in order to understand how the models are created so that they can evaluate them critically and participate actively to minimize current challenges.
“Intelligence Artificielle et Néphrologie” conference, which will be organized on September 14-15 2023 in Paris, will present these challenges faced in the field and emphasize the solutions of AI application.