Barriers to accessing multiple sclerosis disease-modifying therapies in the Middle East and North Africa region: A regional survey-based study

Zeineddine, Maya; Al-Hajje, Amal; Salameh, Pascale; Helme, Anne; Thor, Michael Gunnar; Boumediene, Farid; Yamout, Bassem

doi:10.1016/j.msard.2023.104959

Multiple Sclerosis and Related Disorders

2023

DOI: 10.1016/j.msard.2023.104959

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Barriers to accessing multiple sclerosis disease-modifying therapies in the Middle East and North Africa region: A regional survey-based study

Maya Zeineddine,

Amal Al-Hajje,

Pascale Salameh

et al.

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Cited by 3 publications

References 23 publications

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Can we improve outcomes in MS around the world? Access and global considerations across income strata

Yeh,

Giovannoni,

Hawkes

et al. 2023

Multiple Sclerosis and Related Disorders

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Can we improve outcomes in MS around the world? Access and global considerations across income strata

Yeh,

Giovannoni,

Hawkes

et al. 2023

Multiple Sclerosis and Related Disorders

View full text Add to dashboard Cite

Disease-modifying therapies enter the World Health Organization Essential Medicines List: A victory now requiring a roadmap of implementation

Yamout,

Viswanathan,

Laurson-Doube

et al. 2023

Mult Scler

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Integrating large language models in care, research, and education in multiple sclerosis management

Inojosa,

Voigt,

Wenk

et al. 2024

Mult Scler

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Use of techniques derived from generative artificial intelligence (AI), specifically large language models (LLMs), offer a transformative potential on the management of multiple sclerosis (MS). Recent LLMs have exhibited remarkable skills in producing and understanding human-like texts. The integration of AI in imaging applications and the deployment of foundation models for the classification and prognosis of disease course, including disability progression and even therapy response, have received considerable attention. However, the use of LLMs within the context of MS remains relatively underexplored. LLMs have the potential to support several activities related to MS management. Clinical decision support systems could help selecting proper disease-modifying therapies; AI-based tools could leverage unstructured real-world data for research or virtual tutors may provide adaptive education materials for neurologists and people with MS in the foreseeable future. In this focused review, we explore practical applications of LLMs across the continuum of MS management as an initial scope for future analyses, reflecting on regulatory hurdles and the indispensable role of human supervision.

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Barriers to accessing multiple sclerosis disease-modifying therapies in the Middle East and North Africa region: A regional survey-based study

Cited by 3 publications

References 23 publications

Can we improve outcomes in MS around the world? Access and global considerations across income strata

Can we improve outcomes in MS around the world? Access and global considerations across income strata

Disease-modifying therapies enter the World Health Organization Essential Medicines List: A victory now requiring a roadmap of implementation

Integrating large language models in care, research, and education in multiple sclerosis management

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