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<p>Artificial intelligence (AI) and its deep-learning (DL) sub-field are reshaping the multiple sclerosis (MS) landscape from image acquisition to drug discovery. This narrative review synthesizes recently published evidence that links technical advances with clinical need. Deep-learning platforms now match experienced neuroradiologists, detecting more than 14% of cortical lesions and generating synthetic contrast-enhanced MR images that eliminate the use of gadolinium. Multimodal prognostic engines with MRI, optical-coherence tomography, serum neurofilament light chain (NfL) and smartphone-based gait metrics can predict relapse or conversion to the secondary-progressive phase months and sometimes years before standard review. Multi-omics classifiers further help refine care by identifying which non-responders to natalizumab have a greater than 80 percent probability of not responding to the therapy; and patient-facing apps turn daily symptom logs into patient-specific advice that reduces unplanned visits and raises quality of life scores. In the lab, generative models compress the design cycle of new compounds, and knowledge-graph analytics identify new indications for existing drugs. Yet gains remain uneven: LMIC clinics lack computational infrastructure, demographic bias skews performance, and regulatory frameworks trail algorithmic evolution. In essence, cloud-optimized deployment, participatory data sharing, explainable outputs and shared liability are necessary for ethical, equitable integration. When these pillars align, AI will move from just being an accessory to an essential scaffold, enabling genuinely anticipatory, precision MS care all while increasing the understanding of disease biology.</p>

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From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Akanksha Prasad,
  • Anuradha Sharma

摘要

Artificial intelligence (AI) and its deep-learning (DL) sub-field are reshaping the multiple sclerosis (MS) landscape from image acquisition to drug discovery. This narrative review synthesizes recently published evidence that links technical advances with clinical need. Deep-learning platforms now match experienced neuroradiologists, detecting more than 14% of cortical lesions and generating synthetic contrast-enhanced MR images that eliminate the use of gadolinium. Multimodal prognostic engines with MRI, optical-coherence tomography, serum neurofilament light chain (NfL) and smartphone-based gait metrics can predict relapse or conversion to the secondary-progressive phase months and sometimes years before standard review. Multi-omics classifiers further help refine care by identifying which non-responders to natalizumab have a greater than 80 percent probability of not responding to the therapy; and patient-facing apps turn daily symptom logs into patient-specific advice that reduces unplanned visits and raises quality of life scores. In the lab, generative models compress the design cycle of new compounds, and knowledge-graph analytics identify new indications for existing drugs. Yet gains remain uneven: LMIC clinics lack computational infrastructure, demographic bias skews performance, and regulatory frameworks trail algorithmic evolution. In essence, cloud-optimized deployment, participatory data sharing, explainable outputs and shared liability are necessary for ethical, equitable integration. When these pillars align, AI will move from just being an accessory to an essential scaffold, enabling genuinely anticipatory, precision MS care all while increasing the understanding of disease biology.