The Nuclear Nephrology Artificial Intelligence Ecosystem

Abstract

The contemporary scope of nuclear nephrology extends from non-imaging techniques for glomerular filtration rate calculation through dynamic renal scintigraphy and cortical imaging with planar or single photon emission computed tomography (SPECT) approaches to emerging applications in positron emission tomography (PET) renography and theranostics-based renal toxicity risk assessment. Artificial intelligence (AI) shares a long history with nuclear nephrology that started with expert systems and statistical machine learning (ML) approaches, transitioned through feed forward neural networks (FFNN), landed with convolutional neural networks (CNNs) and deep learning (DL), and has emerging opportunities across the gamut of generative AI like large language models (LLMs), diffusion models, generative adversarial networks (GANs) and multimodal models like vision language models (VLMs). A range of AI tools across the nuclear nephrology ecosystem describe bespoke AI algorithms, commercial AI products, embedded AI tools from vendors, general-purpose and cross-domain AI frameworks. Applications in clinical workflow, research and development, and imaging are explored, highlighting the potential of AI in detection, classification, segmentation, prediction, data analysis and image enhancement. Emerging AI opportunities from generative AI, LLMs, VLMs, and segmentation foundation models such as the SAM, offer exciting multi-modal, few-shot learning that may re-imagine nuclear nephrology. There remains the need for considerable development and validation for widespread clinical utility of AI opportunities, and the need for consideration of ethical limitations and social justice.

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