{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:21:10Z","timestamp":1787728870784,"version":"build-2784847793"},"reference-count":73,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Euro J of Neurology"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Artificial 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.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    To 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.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    The 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\u2010learning modules, podcasts, masterclasses, and interactive sessions at EAN congresses. Also, a recently conducted Europe\u2010wide 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.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    The 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.\n                  <\/jats:p>","DOI":"10.1111\/ene.70728","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T14:22:27Z","timestamp":1787322147000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Navigating the Artificial Intelligence Revolution in Clinical Neurology: A New Multidisciplinary Task Force Within the European Academy of Neurology"],"prefix":"10.1111","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8165-9816","authenticated-orcid":false,"given":"Alice","family":"Accorroni","sequence":"first","affiliation":[{"name":"Geneva Memory Center, Division of Geriatrics, Department of Rehabilitation and Geriatrics Geneva 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