Volume 86, June 2024, 105611Author links open overlay panelAbstract

Multiple sclerosis poses diagnostic and therapeutic challenges for healthcare professionals, with a high risk of misdiagnosis and difficulties in assessing therapeutic effectiveness. Artificial intelligence, particularly machine learning and deep neural networks, emerges as a promising tool to address these challenges. These technologies have the capability to analyze a wide range of data, from magnetic resonance imaging to genetic information, to provide more accurate diagnoses, classify multiple sclerosis subtypes, and predict disease progression and treatment response with extraordinary precision. However, their implementation raises ethical dilemmas, such as accountability in case of errors and the risk of excessive reliance on healthcare personnel. That said, this manuscript aims to urge healthcare professionals dedicated to the care and research of multiple sclerosis patients to recognize artificial intelligence as a valuable and complementary resource in their clinical practice. It also seeks to emphasize the importance of integrating this type of technology safely and responsibly, thereby ensuring the ethics and welfare of patients.

Section snippetsDear editor

Multiple sclerosis (MS) stands as an autoimmune disease of the central nervous system, characterized by an inflammatory process of demyelination and axonal damage, which constitute the pathological substrate of irreversible neurological damage (McGinley et al., 2021). This condition manifests with a wide range of symptoms, ranging from sensory disturbances to gait difficulties, visual problems, intestinal and urinary dysfunction, cognitive and emotional impairment, as well as dizziness,

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That it is an original work.

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That it has not been previously published in whole or in part.

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That it is not under evaluation in any other publication.

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That all authors are responsible for the final version of this article, to the preparation of which they have contributed.

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That the fact of being accepted for publication implies that all authorship rights are transferred.

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That there was not and will not be any economic benefit for the preparation of this manuscript.

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That there was no source of funding

CRediT authorship contribution statement

Sebastián Rodríguez: Conceptualization, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The author does not wish to express gratitude to any institution or individual.

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