Deep Learning for Multiple Sclerosis on AI-Computing Networks: A Systematic Review
Abstract
Recent advances in AI-computing networks (ACN) provide a timely backdrop for assessing deep-learning (DL) research in healthcare. This systematic review synthesizes DL applications in multiple sclerosis (MS) and evaluates their readiness for ACN-enabled deployment. A Web of Science Core Collection search (2014-2024) retrieved 438 records; 264 met stringent inclusion criteria. Bibliometric and knowledge-mapping analyses reveal steady growth in publications and citations, with MRIbased UNet variants dominating lesion-segmentation and diseaseclassification tasks. Emerging themes include gait-sensor analytics, longitudinal progression modelling, quantitative susceptibility mapping, and the growing use of transfer and federated learning to overcome data scarcity and privacy barriers. These resourceaware strategies signal a shift toward distributed training and inference paradigms that align naturally with ACN architectures. Nevertheless, few studies report multi-institutional experiments or network-level performance metrics, underscoring the need for tighter integration between DL methods and ACN infrastructure. We highlight research gaps-particularly in cross-site model orchestration and low-latency, on-device inference-that AIcomputing networks are well positioned to address, enabling scalable and interoperable DL services for MS diagnosis and prognosis.
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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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