Multiple sclerosis and related disorders · 2026
Implications of artificial intelligence in multiple sclerosis theragnostics: current advances and barriers to clinical translation.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Noor Hooria, Humayun Shaafay, Durrani Abrahim Danish, Ahmad Ahsan
Аннотация
BACKGROUND: Artificial intelligence (AI). has been applied across many aspects of multiple sclerosis (MS). theragnostics, from lesion detection in magnetic resonance imaging (MRI)., gait assessment and treatment response prediction to drug repurposing. However, a considerable number of models characterized by high technical performance have yet to be translated into a clinical setting.
OBJECTIVE: To investigate the translational barriers between AI-based theragnostic applications and their clinical implementation in MS.
KEY MESSAGES: Many AI-based models have been trained using retrospective data from a single-center source and are rarely externally validated in larger independent populations. Major barriers to translation include technical issues, limited prospective validation, and multi-modality data, poor interoperability, clinical utility, and ethical concerns. In MS particularly, models must remain reliable across various MS phenotypes and disease modifying therapies making translation even more challenging.
CONCLUSION: High technical performance alone is not sufficient for clinical implementation. Meaningful translation of AI-based theragnostic approaches in MS will require multicenter datasets representative of larger populations, further external validation and transparency. Only through these measures can AI-driven theragnostic approaches evolve from promising research models into clinically meaningful tools for MS care.
OBJECTIVE: To investigate the translational barriers between AI-based theragnostic applications and their clinical implementation in MS.
KEY MESSAGES: Many AI-based models have been trained using retrospective data from a single-center source and are rarely externally validated in larger independent populations. Major barriers to translation include technical issues, limited prospective validation, and multi-modality data, poor interoperability, clinical utility, and ethical concerns. In MS particularly, models must remain reliable across various MS phenotypes and disease modifying therapies making translation even more challenging.
CONCLUSION: High technical performance alone is not sufficient for clinical implementation. Meaningful translation of AI-based theragnostic approaches in MS will require multicenter datasets representative of larger populations, further external validation and transparency. Only through these measures can AI-driven theragnostic approaches evolve from promising research models into clinically meaningful tools for MS care.