A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare
Abstract
Concerns regarding energy use, environmental effects, and long-term sustainability have been highlighted in recent years by the expanding application of Artificial Intelligence (AI) in healthcare. This systematic review paper categorizes and classifies AI algorithms and tools in the healthcare sector to support more sustainable practices, focusing on reducing energy use while maintaining high standards in diagnostic accuracy and patient outcomes. AI algorithms and tools are categorized into three groups: explicit AI algorithms for sustainability for energy efficiency (e.g., Federated Learning, Hybrid Quantum-Classical Optimization, Modified Lempel-Ziv-Welch (mLZW)), traditional AI algorithms for sustainable healthcare (e.g., Bidirectional Long Short-Term Memory (Bi-LSTM), Backpropagation Neural Networks (BPNNs), Convolutional Neural Networks (CNNs)), and sustainable AI techniques (e.g., Adaptive Sampling, AutoML for Model Compression (AMC)) that support low-power computing (e.g., edge computing, neuromorphic hardware, adaptive sampling). A comprehensive performance analysis is presented across five dimensions: energy consumption, latency, accuracy, complexity, and cost. The review highlights mLZW as promising for energy efficiency, complexity, and cost, OFA for low-latency deployment, and Hybrid Quantum Classical Optimization for diagnostic accuracy. We propose an integration framework for deploying these methods in resource-constrained healthcare environments, identifying…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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