arXiv:2606.07553v1 Announce Type: new Abstract: The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare. However, this rapid adoption has come at the cost of requiring significant computing power and energy consumption, as well as e-waste disposal and carbon emissions. One of the challenges of these models is choosing the right model for classification tasks. To this end, researchers attempt to identify the optimal model using their data through trial and error, which involves energy consumption
The increasing adoption of AI in healthcare is prompting a critical examination of its computational cost and environmental impact, driving innovation towards more efficient model deployment.
This research addresses the significant computational and energy burden of current AI models in healthcare, offering a path to more sustainable and accessible AI diagnostics without constant retraining.
The ability to deploy medical AI models effectively without extensive retraining reduces operational costs, energy consumption, and democratizes advanced diagnostic capabilities.
- · Healthcare providers
- · Patients
- · AI developers focused on efficiency
- · Cloud computing providers (reduced egress/compute for model updates)
- · Companies relying on high-frequency model retraining for revenue
- · Energy-inefficient AI hardware manufacturers
Reduced computational demand and energy consumption for AI in medical imaging.
Faster, more cost-effective deployment and updates of diagnostic AI, leading to broader adoption in resource-constrained environments.
Shifts in AI architecture research towards energy efficiency and 'zero-shot' or 'few-shot' learning paradigms for healthcare applications.
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Read at arXiv cs.LG