Evidence map›Paper›PMID 42627286›Full record

ArticleEuropean journal of neurology2026

Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology.

Alice Accorroni, Raphael Wurm, Roland Wiest, Marcello Ienca, Raphael Bernard-Valnet, Giacomo Sferruzza, Tony Marson, Giuseppe Jurman, James Teo, Elena Moro and 2 more

Abstract read
In one paragraph

Article in European journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Alice AccorroniGeneva Memory Center, Division of Geriatrics, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0000-0001-8165-9816
Raphael WurmDepartment of Neurology, Medical University of Vienna, Vienna, Austria.ORCID https://orcid.org/0000-0003-3027-7775
Roland WiestUniversity Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0001-7030-2045
Marcello IencaLaboratory of Ethics of AI & Neuroscience, Institute for History and Ethics of Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Raphael Bernard-ValnetService of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (Centre Hospitalier Universitaire Vaudois) and University of Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0000-0001-7447-344X
Giacomo SferruzzaVita-Salute San Raffaele University, Milan, Italy.ORCID https://orcid.org/0000-0003-2360-4803
Tony MarsonInstitute of Systems, Molecular and Integrative Biology (ISMIB), University of Liverpool, Liverpool, UK.
Giuseppe JurmanHumanitas University, Milan, Italy.ORCID https://orcid.org/0000-0002-2705-5728
James TeoKing's College Hospital NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0002-6899-8319
Elena MoroDivision of Neurology, CHU of Grenoble, Grenoble Institute of Neurosciences, INSERM, Grenoble Alpes University, Grenoble, France.ORCID https://orcid.org/0000-0002-7968-5908
Nathalie NasrDepartment of Neurology, Poitiers University Hospital, INSERM 1084 Neurosciences, Poitiers, France.
Maria Chiara MalagutiDepartment of Neurology, Santa Maria Del Carmine Hospital, Azienda Sanitaria Universitaria Integrata Del Trentino, Rovereto, Italy.ORCID https://orcid.org/0000-0002-4807-4063

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is rapidly transforming clinical neurology, offering significant potential to enhance diagnosis, treatment, and disease management. Despite this transformative promise, AI adoption in neurology remains limited due to a lack of a reliable evidence base to support its use, as well as educational, ethical, methodological, and regulatory barriers. Furthermore, currently, there is no standardized educational framework for the application of AI in clinical neurology across Europe.

methodsTo address this gap, the European Academy of Neurology (EAN) has established a dedicated multidisciplinary Task Force (TF) on AI in Clinical Neurology. This TF involves neurologists, neurology trainees, medical students, computer and data scientists, ethicists, patient representatives, and regulatory experts.

resultsThe AI TF has designed a modular curriculum covering technical AI foundations and models, clinical applications, ethical implications, and regulatory compliance. Planned educational resources include e-learning modules, podcasts, masterclasses, and interactive sessions at EAN congresses. Also, a recently conducted Europe-wide survey supported the TF in identifying current knowledge levels and educational and systemic barriers, informing the design of targeted interventions. In parallel, the TF will formulate recommendations addressing the ethical and regulatory implications of AI use in neurology, tailored to the specific needs of clinicians.

conclusionsThe EAN TF on AI in Clinical Neurology represents a strategic initiative to enable responsible AI integration through multidisciplinary collaboration. By closing educational gaps, establishing clear ethical standards, and facilitating stakeholder engagement, the TF aims to empower neurologists to confidently and ethically adopt AI technologies, ultimately improving patient care across Europe.

Indexed as

Advisory CommitteesArtificial IntelligenceNeurologyAcademies and InstitutesCurriculumEuropeHumansartificial intelligenceclinical neurologyeducationEuropean academy of neurology

Identifiers

PMID42627286
PMCPMC13495984

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.