Artificial intelligence-based tools have the potential to transform health care, enabling faster and more accurate diagnosis, personalized treatment plans, new therapeutic approaches and effective disease monitoring. Artificial intelligence shows particular promise for the management of rare neurological disorders by augmenting knowledge and facilitating the sharing of expertise among physicians.
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References
Schaefer, J., Lehne, M., Schepers, J., Prasser, F. & Thun, S. The use of machine learning in rare diseases: a scoping review. Orphanet J. Rare Dis. 15, 145 (2020).
Yuan, X. et al. Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. Brief. Bioinform. 23, bbac019 (2022).
Groza, T. et al. The Human Phenotype Ontology: semantic unification of common and rare disease. Am. J. Hum. Genet. 97, 111–124 (2015).
Lin, S. et al. An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease. Front. Neurol. 14, 1108222 (2023).
Visibelli, A., Roncaglia, B., Spiga, O. & Santucci, A. The impact of artificial intelligence in the odyssey of rare diseases. Biomedicines 11, 887 (2023).
Kabeya, Y. et al. Deep convolutional neural network-based algorithm for muscle biopsy diagnosis. Lab. Invest. 102, 220–226 (2022).
Dias, R. & Torkamani, A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 11, 70 (2019).
Gurovich, Y. et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat. Med. 25, 60–64 (2019).
Girdea, M. et al. PhenoTips: patient phenotyping software for clinical and research use. Hum. Mutat. 34, 1057–1065 (2013).
Bakkar, N. et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol. 135, 227–247 (2018).
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Molnar, M.J., Molnar, V. AI-based tools for the diagnosis and treatment of rare neurological disorders. Nat Rev Neurol 19, 455–456 (2023). https://doi.org/10.1038/s41582-023-00841-y
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DOI: https://doi.org/10.1038/s41582-023-00841-y