دورية أكاديمية

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

التفاصيل البيبلوغرافية
العنوان: Artificial intelligence in neurology: opportunities, challenges, and policy implications.
المؤلفون: Voigtlaender S; Systems Neuroscience Division, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.; Virtual Diagnostics Team, QuantCo Inc., Cambridge, MA, USA., Pawelczyk J; Faculty of Medicine, Ruprecht-Karls-University, Heidelberg, Germany.; Graduate Center of Medicine and Health, Technical University Munich, Munich, Germany., Geiger M; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.; NVIDIA, Zurich, Switzerland., Vaios EJ; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA., Karschnia P; Department of Neurosurgery, Ludwig-Maximilians-University and University Hospital Munich, Munich, Germany.; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Cudkowicz M; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Dietrich J; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Haraldsen IRJH; Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway., Feigin V; National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand., Owolabi M; Center for Genomics and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.; Neurology Unit, Department of Medicine, University of Ibadan, Ibadan, Nigeria.; Blossom Specialist Medical Center, Ibadan, Nigeria.; Lebanese American University of Beirut, Beirut, Lebanon., White TL; Department of Behavioral and Social Sciences, Brown University, Providence, RI, USA., Świeboda P; Human Brain Project, European Union, Brussels, Belgium., Farahany N; Duke University School of Law, Durham, NC, USA., Natarajan V; Google Research, Mountain View, CA, USA., Winter SF; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. sfwinter@mgh.harvard.edu.
المصدر: Journal of neurology [J Neurol] 2024 May; Vol. 271 (5), pp. 2258-2273. Date of Electronic Publication: 2024 Feb 17.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 0423161 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1459 (Electronic) Linking ISSN: 03405354 NLM ISO Abbreviation: J Neurol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Berlin ; New York, Springer-Verlag
مواضيع طبية MeSH: Artificial Intelligence* , Neurology*/methods, Humans ; Health Policy ; Nervous System Diseases/therapy ; Nervous System Diseases/diagnosis
مستخلص: Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)
References: GBD 2019 Stroke Collaborators (2021) Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 20(10):795–820. https://doi.org/10.1016/s1474-4422(21)00252-0. (PMID: 10.1016/s1474-4422(21)00252-0)
GBD Neurology Collaborators (2019) Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18(5):459–480. https://doi.org/10.1016/S1474-4422(18)30499-X. (PMID: 10.1016/S1474-4422(18)30499-X)
Winkler AS (2020) The growing burden of neurological disorders in low-income and middle-income countries: priorities for policy making. Lancet Neurol 19(3):200–202. https://doi.org/10.1016/S1474-4422(19)30476-4. (PMID: 10.1016/S1474-4422(19)30476-431813849)
World Health Organization, Draft Intersectoral global action plan on epilepsy and other neurological disorders 2022–2031, 20 July 2023. [Online]. Available: https://www.who.int/news/item/28-04-2022-draft-intersectoral-global-action-plan-on-epilepsy-and-other-neurological-disorders-2022-2031 . Accessed 20 Jan 2024.
Owolabi MO, Leonardi M, Bassetti C, Jaarsma J, Hawrot T, Makanjuola AI (2022) The neurology revolution. Lancet Neurol 21(11):960–961. https://doi.org/10.1016/s1474-4422(22)00394-5. (PMID: 10.1016/s1474-4422(22)00394-536137553)
Pandarinath C et al (2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nat Methods 15(10):805–815. https://doi.org/10.1038/s41592-018-0109-9. (PMID: 10.1038/s41592-018-0109-9302246736380887)
Défossez A, Caucheteux C, Rapin J, Kabeli O, King J-R (2023) Decoding speech perception from non-invasive brain recordings. Nat Mach Intell 5(10):1097–1107. https://doi.org/10.1038/s42256-023-00714-5. (PMID: 10.1038/s42256-023-00714-5)
Gupta A, Vardalakis N, Wagner FB (2023) Neuroprosthetics: from sensorimotor to cognitive disorders. Commun Biol 6(1):14. https://doi.org/10.1038/s42003-022-04390-w. (PMID: 10.1038/s42003-022-04390-w366095599823108)
Hausmann D et al (2023) Autonomous rhythmic activity in glioma networks drives brain tumour growth. Nature 613(7942):179–186. https://doi.org/10.1038/s41586-022-05520-4. (PMID: 10.1038/s41586-022-05520-436517594)
Monje M et al (2020) Roadmap for the emerging field of cancer neuroscience. Cell 181(2):219–222. https://doi.org/10.1016/j.cell.2020.03.034. (PMID: 10.1016/j.cell.2020.03.034323025647286095)
Winter SF et al (2023) Uniting for global brain health: where advocacy meets awareness. Epilepsy Behav 145:109295. https://doi.org/10.1016/j.yebeh.2023.109295. (PMID: 10.1016/j.yebeh.2023.10929537348407)
Owolabi MO et al (2023) Global synergistic actions to improve brain health for human development. Nat Rev Neurol. https://doi.org/10.1038/s41582-023-00808-z. (PMID: 10.1038/s41582-023-00808-z3720849610197060)
Lancet T (2021) Brain health and its social determinants. Lancet 398(10305):1021. https://doi.org/10.1016/s0140-6736(21)02085-7. (PMID: 10.1016/s0140-6736(21)02085-7)
Winter SF et al (2022) Brain health-directed policymaking: a new concept to strengthen democracy. Brookings Institution.
Materializing artificial intelligence. Nature Machine Intelligence 2(11): 653, 2020 https://doi.org/10.1038/s42256-020-00262-2.
Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) AI in health and medicine. Nat Med 28(1):31–38. https://doi.org/10.1038/s41591-021-01614-0. (PMID: 10.1038/s41591-021-01614-035058619)
Wang H et al (2023) Scientific discovery in the age of artificial intelligence. Nature 620(7972):47–60. https://doi.org/10.1038/s41586-023-06221-2. (PMID: 10.1038/s41586-023-06221-237532811)
Bommasani R et al (2021) On the opportunities and risks of foundation models, arXiv [cs.LG], 2021/8/16. [Online]. Available: http://arxiv.org/abs/2108.07258 . Accessed 20 Jan 2024.
Singhal K et al (2023) Large language models encode clinical knowledge. Nature. https://doi.org/10.1038/s41586-023-06291-2. (PMID: 10.1038/s41586-023-06291-23750097910412443)
Tu T et al. (2023) Towards generalist biomedical ai, arXiv preprint arXiv:2307.14334 .
Sudlow C et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):e1001779. https://doi.org/10.1371/journal.pmed.1001779. (PMID: 10.1371/journal.pmed.1001779258263794380465)
Zador A et al (2023) Catalyzing next-generation Artificial Intelligence through Neuro AI. Nat Commun 14(1):1597. https://doi.org/10.1038/s41467-023-37180-x. (PMID: 10.1038/s41467-023-37180-x3694904810033876)
Hassabis D, Kumaran D, Summerfield C, Botvinick M (2017) Neuroscience-inspired artificial intelligence. Neuron 95(2):245–258. https://doi.org/10.1016/j.neuron.2017.06.011. (PMID: 10.1016/j.neuron.2017.06.01128728020)
Haug CJ, Drazen JM (2023) Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med 388(13):1201–1208. https://doi.org/10.1056/NEJMra2302038. (PMID: 10.1056/NEJMra230203836988595)
Subbiah V (2023) The next generation of evidence-based medicine. Nat Med. https://doi.org/10.1038/s41591-022-02160-z. (PMID: 10.1038/s41591-022-02160-z3705983410202803)
Moor M et al (2023) Foundation models for generalist medical artificial intelligence. Nature 616(7956):259–265. https://doi.org/10.1038/s41586-023-05881-4. (PMID: 10.1038/s41586-023-05881-437045921)
Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ (2022) Multimodal biomedical AI. Nat Med 28(9):1773–1784. https://doi.org/10.1038/s41591-022-01981-2. (PMID: 10.1038/s41591-022-01981-236109635)
Vandereyken K, Sifrim A, Thienpont B, Voet T (2023) Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet. https://doi.org/10.1038/s41576-023-00580-2. (PMID: 10.1038/s41576-023-00580-2368641789979144)
Yang J et al (2022) DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas. Nat Commun 13(1):4410. https://doi.org/10.1038/s41467-022-31827-x. (PMID: 10.1038/s41467-022-31827-x359062139338285)
Roussarie J-P et al (2020) Selective neuronal vulnerability in Alzheimer’s disease: a network-based analysis. Neuron 107(5):821-835.e12. https://doi.org/10.1016/j.neuron.2020.06.010. (PMID: 10.1016/j.neuron.2020.06.010326036557580783)
US Food and Drug Administration. Rapid ASPECTS, iSchemaView, Inc. 510(k) Summary (K200760). https://www.accessdata.fda.gov/cdrh_docs/pdf20/K200760.pdf . Accessed 16 Jan 2024.
Albers GW et al (2019) Automated calculation of alberta stroke program early CT score: validation in patients with large hemispheric infarct. Stroke 50(11):3277–3279. https://doi.org/10.1161/strokeaha.119.026430. (PMID: 10.1161/strokeaha.119.02643031500555)
US Food and Drug Administration. StrokeSENS LVO, Circle Neurovascular Imaging, Inc. 510(k) Summary (K212261). https://www.accessdata.fda.gov/cdrh_docs/pdf21/K212261.pdf . Accessed 16 Jan 2024.
US Food and Drug Administration. FastStroke, CT Perfusion 4D, GE Medical Systems SCS 510(k) Summary (K193289). https://www.accessdata.fda.gov/cdrh_docs/pdf19/K193289.pdf . Accessed 16 Jan 2024.
US Food and Drug Administration. NeuroRPM, New Touch Digital, Inc. 510(k) Summary (K220437). https://www.accessdata.fda.gov/cdrh_docs/pdf22/K220437.pdf . Accessed 16 Jan 2024.
Cognixion Inc. CXN ONE. https://one.cognixion.com/ . Accessed 17 Jan 2024.
Cognixion Inc. Cognixion Receives FDA Breakthrough Device Designation. https://www.cognixion.com/blog/2023/5/3/cognixion-receives-fda-breakthrough-device-designation-for-its-brain-computer-interface-with-augmented-reality-for-assistive-communication . Accessed 17 Jan 2024.
Barnett M et al (2023) A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis. npj Digital Med 6(1):196. https://doi.org/10.1038/s41746-023-00940-6. (PMID: 10.1038/s41746-023-00940-6)
US Food and Drug Administration. Neurophet AQUA, NEUROPHET, Inc. 510(k) Summary (K203235). https://www.accessdata.fda.gov/cdrh_docs/pdf20/K203235.pdf . Accessed 16 Jan 2024.
Wang JY et al (2023) Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery. Radiat Oncol 18(1):61. https://doi.org/10.1186/s13014-023-02246-z. (PMID: 10.1186/s13014-023-02246-z3701641610074777)
US Food and Drug Administration. Persyst 15 EEG Review and Analysis Software, Persyst Development Corporation 510(k) Summary (K222002). https://www.accessdata.fda.gov/cdrh_docs/pdf22/K222002.pdf . Accessed 16 Jan 2024.
Ganguly TM et al (2022) Seizure detection in continuous inpatient EEG: a comparison of human vs automated review. Neurology 98(22):e2224–e2232. https://doi.org/10.1212/wnl.0000000000200267. (PMID: 10.1212/wnl.0000000000200267354109059162163)
US Food and Drug Administration. Ceribell Status Epilepticus Monitor, Ceribell, Inc. 510(k) Summary (K223504). https://www.accessdata.fda.gov/cdrh_docs/pdf22/K223504.pdf . Accessed 16 Jan 2024.
US Food and Drug Administration. Brainscope TBI, Brainscope Company, Inc. 510(k) Summary (K190815). https://www.accessdata.fda.gov/cdrh_docs/pdf19/K190815.pdf . Accessed 16 Jan 2024.
US Food and Drug Administration. EyeBOX (Model EBX-4), Oculogica, Inc. 510(k) Summary (K212310). https://www.accessdata.fda.gov/cdrh_docs/pdf21/K212310.pdf . Accessed 16 Jan 2024.
US Food and Drug Administration. 7D Surgical System Cranial Biopsy and Ventricular Catheter Placement Application, 7D Surgical, Inc. 501(k) Summary (K192945). https://www.accessdata.fda.gov/cdrh_docs/pdf19/K192945.pdf . Accessed 17 Jan 2024.
US Food and Drug Administration. EarliPoint System, EarliTec Diagnostics, Inc. 510(k) Summary (K213882). https://www.accessdata.fda.gov/cdrh_docs/pdf21/K213882.pdf . Accessed 16 Jan 2024.
US Food and Drug Administration. Cognoa ASD Diagnosis Aid, Cognoa, Inc. 510(k) Summary (DEN200069). https://www.accessdata.fda.gov/cdrh_docs/pdf20/DEN200069.pdf Accessed 16 Jan 2024.
Palla G et al (2022) Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19(2):171–178. https://doi.org/10.1038/s41592-021-01358-2. (PMID: 10.1038/s41592-021-01358-2351023468828470)
Tu T et al. (2024) Towards conversational diagnostic AI, arXiv preprint arXiv:2401.05654 . Accessed 20 Jan 2024.
Nuance Inc. Nuance announces the general availability of dragon ambient experience copilot to further improve healthcare experiences, outcomes, and efficiency. https://news.nuance.com/2023-09-27-Nuance-Announces-the-General-Availability-of-Dragon-Ambient-eXperience-Copilot-to-Further-Improve-Healthcare-Experiences,-Outcomes,-and-Efficiency . Accessed 17 Jan 2024.
World Health Organization, Optimizing brain health across the life course: WHO position paper, 9 August 2022. [Online]. Available: https://www.who.int/publications/i/item/9789240054561 . Accessed 16 Jan 2024.
Feigin VL, Owolabi MO (2023) Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization-Lancet Neurology Commission. Lancet Neurol. https://doi.org/10.1016/s1474-4422(23)00277-6. (PMID: 10.1016/s1474-4422(23)00277-637827183)
Williams GJ et al (2023) Wearable technology and the cardiovascular system: the future of patient assessment. Lancet Digit Health 5(7):e467–e476. https://doi.org/10.1016/s2589-7500(23)00087-0. (PMID: 10.1016/s2589-7500(23)00087-037391266)
Nes BM, Gutvik CR, Lavie CJ, Nauman J, Wisløff U (2017) Personalized activity intelligence (PAI) for prevention of cardiovascular disease and promotion of physical activity. Am J Med 130(3):328–336. https://doi.org/10.1016/j.amjmed.2016.09.031. (PMID: 10.1016/j.amjmed.2016.09.03127984009)
Blauwendraat C, Nalls MA, Singleton AB (2020) The genetic architecture of Parkinson’s disease. Lancet Neurol 19(2):170–178. https://doi.org/10.1016/S1474-4422(19)30287-X. (PMID: 10.1016/S1474-4422(19)30287-X31521533)
Zhou X et al (2023) Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction. Commun Med (Lond) 3(1):49. https://doi.org/10.1038/s43856-023-00269-x. (PMID: 10.1038/s43856-023-00269-x37024668)
Zhu Z et al (2022) Retinal age gap as a predictive biomarker of stroke risk. BMC Med 20(1):466. https://doi.org/10.1186/s12916-022-02620-w. (PMID: 10.1186/s12916-022-02620-w364472939710167)
Feigin VL et al (2020) The global burden of neurological disorders: translating evidence into policy. Lancet Neurol 19(3):255–265. https://doi.org/10.1016/s1474-4422(19)30411-9. (PMID: 10.1016/s1474-4422(19)30411-931813850)
Liu CF et al (2021) Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke. Commun Med (Lond) 1:61. https://doi.org/10.1038/s43856-021-00062-8. (PMID: 10.1038/s43856-021-00062-835602200)
Brugnara G et al (2023) Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke. Nat Commun 14(1):4938. https://doi.org/10.1038/s41467-023-40564-8. (PMID: 10.1038/s41467-023-40564-83758282910427649)
Lohmann P et al (2022) Radiomics in neuro-oncological clinical trials. Lancet Digit Health 4(11):e841–e849. https://doi.org/10.1016/s2589-7500(22)00144-3. (PMID: 10.1016/s2589-7500(22)00144-336182633)
Leone R et al (2023) Assessing the added value of apparent diffusion coefficient, cerebral blood volume, and radiomic magnetic resonance features for differentiation of pseudoprogression versus true tumor progression in patients with glioblastoma. Neurooncol Adv 5(1):vdad016. https://doi.org/10.1093/noajnl/vdad016. (PMID: 10.1093/noajnl/vdad0163696829110034916)
Park B-Y et al (2022) Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy. Brain 145(4):1285–1298. https://doi.org/10.1093/brain/awab417. (PMID: 10.1093/brain/awab41735333312)
Pujadas ER et al (2023) Prediction of incident cardiovascular events using machine learning and CMR radiomics. Eur Radiol 33(5):3488–3500. https://doi.org/10.1007/s00330-022-09323-z. (PMID: 10.1007/s00330-022-09323-z36512045)
Calabrese E et al (2022) Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neurooncol Adv 4(1):vdac060. https://doi.org/10.1093/noajnl/vdac060. (PMID: 10.1093/noajnl/vdac060356112699122791)
Yuan Y et al (2023) Convolutional neural network to predict IDH mutation status in glioma from chemical exchange saturation transfer imaging at 7 Tesla. Front Oncol 13:1134626. https://doi.org/10.3389/fonc.2023.1134626. (PMID: 10.3389/fonc.2023.11346263722367710200907)
Duan J et al (2023) Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: a multicenter study. Mol Oncol 17(4):629–646. https://doi.org/10.1002/1878-0261.13380. (PMID: 10.1002/1878-0261.133803668863310061289)
Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 298(3):505–516. https://doi.org/10.1148/radiol.2021202553. (PMID: 10.1148/radiol.202120255333399513)
Yang Y et al (2022) Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals. Nat Med 28:1–9. https://doi.org/10.1038/s41591-022-01932-x. (PMID: 10.1038/s41591-022-01932-x)
Kadirvelu B et al (2023) A wearable motion capture suit and machine learning predict disease progression in Friedreich’s ataxia. Nat Med 29(1):86–94. https://doi.org/10.1038/s41591-022-02159-6. (PMID: 10.1038/s41591-022-02159-6366584209873563)
Tang J et al (2021) Seizure detection using wearable sensors and machine learning: setting a benchmark. Epilepsia 62(8):1807–1819. https://doi.org/10.1111/epi.16967. (PMID: 10.1111/epi.16967342687288457135)
Schalkamp AK, Peall KJ, Harrison NA, Sandor C (2023) Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nat Med. https://doi.org/10.1038/s41591-023-02440-2. (PMID: 10.1038/s41591-023-02440-237400639)
Karabayir I et al (2022) Predicting Parkinson’s disease and its pathology via simple clinical variables. J Parkinsons Dis 12(1):341–351. https://doi.org/10.3233/JPD-212876. (PMID: 10.3233/JPD-212876346025028842767)
Cheung CY et al (2022) A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health 4(11):e806–e815. https://doi.org/10.1016/S2589-7500(22)00169-8. (PMID: 10.1016/S2589-7500(22)00169-836192349)
Catanese A et al (2023) Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis. Brain. https://doi.org/10.1093/brain/awad075. (PMID: 10.1093/brain/awad0753688364310473564)
Hollon T et al (2023) Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nat Med 29(4):828–832. https://doi.org/10.1038/s41591-023-02252-4. (PMID: 10.1038/s41591-023-02252-43695942210445531)
Ma J, Wang B (2023) Segment anything in medical images, arXiv preprint arXiv:2304.12306 . Accessed 20 Jan 2024.
Zhou Y et al (2023) A foundation model for generalizable disease detection from retinal images. Nature 622(7981):156–163. https://doi.org/10.1038/s41586-023-06555-x. (PMID: 10.1038/s41586-023-06555-x3770472810550819)
Peng C et al (2023) A study of generative large language model for medical research and healthcare. npj Digital Med 6(1):210. https://doi.org/10.1038/s41746-023-00958-w. (PMID: 10.1038/s41746-023-00958-w)
Gaffney A et al (2022) Medical documentation burden among US office-based physicians in 2019: a national study. JAMA Intern Med 182(5):564–566. https://doi.org/10.1001/jamainternmed.2022.0372. (PMID: 10.1001/jamainternmed.2022.0372353440068961402)
Hampel H et al (2023) The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci 46(3):176–198. https://doi.org/10.1016/j.tins.2022.12.004. (PMID: 10.1016/j.tins.2022.12.0043664262610720395)
Migliozzi S et al (2023) Integrative multi-omics networks identify PKCδ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy. Nat Cancer 4(2):181–202. https://doi.org/10.1038/s43018-022-00510-x. (PMID: 10.1038/s43018-022-00510-x367326349970878)
Lam KHB et al (2022) Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity. Nat Commun 13(1):116. https://doi.org/10.1038/s41467-021-27667-w. (PMID: 10.1038/s41467-021-27667-w350132278748638)
Vatansever S et al (2021) Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Med Res Rev 41(3):1427–1473. https://doi.org/10.1002/med.21764. (PMID: 10.1002/med.2176433295676)
San O (2021) The digital twin revolution. Nat Comput Sci 1(5):307–308. https://doi.org/10.1038/s43588-021-00077-0. (PMID: 10.1038/s43588-021-00077-038217208)
Jirsa V et al (2023) Personalised virtual brain models in epilepsy. Lancet Neurol 22(5):443–454. https://doi.org/10.1016/S1474-4422(23)00008-X. (PMID: 10.1016/S1474-4422(23)00008-X36972720)
Zheng Y, Carrillo-Perez F, Pizurica M, Heiland DH, Gevaert O (2023) Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun 14(1):4122. https://doi.org/10.1038/s41467-023-39933-0. (PMID: 10.1038/s41467-023-39933-03743381710336135)
Falet JR et al (2022) Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning. Nat Commun 13(1):5645. https://doi.org/10.1038/s41467-022-33269-x. (PMID: 10.1038/s41467-022-33269-x361633499512913)
Dietz N, Vaitheesh J, Alkin V, Mettille J, Boakye M, Drazin D (2022) Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): a systematic review. J Clin Orthop Trauma 35:102046. https://doi.org/10.1016/j.jcot.2022.102046. (PMID: 10.1016/j.jcot.2022.102046364252819678757)
Claassen J et al (2019) Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 380(26):2497–2505. https://doi.org/10.1056/NEJMoa1812757. (PMID: 10.1056/NEJMoa181275731242361)
Bonkhoff AK, Grefkes C (2022) Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 145(2):457–475. https://doi.org/10.1093/brain/awab439. (PMID: 10.1093/brain/awab43934918041)
Winter SF, Vaios EJ, Dietrich J (2020) Central nervous system injury from novel cancer immunotherapies. Curr Opin Neurol 33(6):723–735. https://doi.org/10.1097/wco.0000000000000867. (PMID: 10.1097/wco.000000000000086732941192)
Tang SJ, Holle J, Lesslar O, Teo C, Sughrue M, Yeung J (2022) Improving quality of life post-tumor craniotomy using personalized, parcel-guided TMS: safety and proof of concept. J Neurooncol 160(2):413–422. https://doi.org/10.1007/s11060-022-04160-y. (PMID: 10.1007/s11060-022-04160-y36308593)
Patel M et al (2021) Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma. Clin Radiol 76(8):628.e17-628.e27. https://doi.org/10.1016/j.crad.2021.03.019. (PMID: 10.1016/j.crad.2021.03.01933941364)
Moses DA et al (2021) Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N Engl J Med 385(3):217–227. https://doi.org/10.1056/NEJMoa2027540. (PMID: 10.1056/NEJMoa2027540342608358972947)
Anumanchipalli GK, Chartier J, Chang EF (2019) Speech synthesis from neural decoding of spoken sentences. Nature 568(7753):493–498. https://doi.org/10.1038/s41586-019-1119-1. (PMID: 10.1038/s41586-019-1119-1310193179714519)
Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV (2021) High-performance brain-to-text communication via handwriting. Nature 593(7858):249–254. https://doi.org/10.1038/s41586-021-03506-2. (PMID: 10.1038/s41586-021-03506-2339810478163299)
Tang J, LeBel A, Jain S, Huth AG (2023) Semantic reconstruction of continuous language from non-invasive brain recordings. Nat Neurosci 26(5):858–866. https://doi.org/10.1038/s41593-023-01304-9. (PMID: 10.1038/s41593-023-01304-937127759)
Farahany NA (2023) The battle for your brain: defending the right to think freely in the age of neurotechnology. St Martin’s Press, New York.
Tomašev N et al (2021) Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nat Protoc 16(6):2765–2787. https://doi.org/10.1038/s41596-021-00513-5. (PMID: 10.1038/s41596-021-00513-533953393)
Agarwal N, Moehring A, Rajpurkar P, Salz T (2023) Combining human expertise with artificial intelligence: experimental evidence from radiology (No. w31422). National Bureau of Economic Research.
US Food and Drug Administration. Artificial intelligence and machine learning in software as a medical device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device . Accessed 18 Jan 2024.
Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686–707. https://doi.org/10.1016/j.jcp.2018.10.045. (PMID: 10.1016/j.jcp.2018.10.045)
Bronstein MM, Bruna J, Cohen T, Veličković P (2021) Geometric deep learning: grids, groups, graphs, geodesics, and gauges, arXiv [cs.LG], 2021/4/27. [Online]. Available: http://arxiv.org/abs/2104.13478 . Accessed 20 Jan 2024.
Overgaard SM et al (2023) Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions. npj Digital Med 6(1):218. https://doi.org/10.1038/s41746-023-00968-8. (PMID: 10.1038/s41746-023-00968-8)
OECD, Recommendation of the Council on Responsible Innovation in Neurotechnology, 11 December 2019. [Online]. Available: https://www.oecd.org/science/recommendation-on-responsible-innovation-in-neurotechnology.htm .
Karschnia P et al (2023) "Prognostic validation of a new classification system for extent of resection in glioblastoma: a report of the RANO resect group. Neuro Oncol 25(5):940–954. https://doi.org/10.1093/neuonc/noac193. (PMID: 10.1093/neuonc/noac19335961053)
World Health Organization, Ethics and governance of artificial intelligence for health: WHO guidance, 28 July 2021. [Online]. Available: https://www.who.int/publications/i/item/9789240029200 . Accessed 20 Jan 2024.
Gilbert S, Harvey H, Melvin T, Vollebregt E, Wicks P (2023) Large language model AI chatbots require approval as medical devices. Nat Med. https://doi.org/10.1038/s41591-023-02412-6. (PMID: 10.1038/s41591-023-02412-637460755)
Chung S, Abbott LF (2021) Neural population geometry: an approach for understanding biological and artificial neural networks. Curr Opin Neurobiol 70:137–144. https://doi.org/10.1016/j.conb.2021.10.010. (PMID: 10.1016/j.conb.2021.10.0103480178710695674)
Ma D et al (2013) Magnetic resonance fingerprinting. Nature 495(7440):187–192. https://doi.org/10.1038/nature11971. (PMID: 10.1038/nature11971234860583602925)
Murray JM, Wiegand B, Hadaschik B, Herrmann K, Kleesiek J (2022) Virtual biopsy: just an AI software or a medical procedure? J Nucl Med 63(4):511–513. https://doi.org/10.2967/jnumed.121.263749. (PMID: 10.2967/jnumed.121.263749351450148973284)
European Union, Ethics by design and ethics of use approaches for artificial intelligence, 2021.
Rieke N et al (2020) The future of digital health with federated learning. NPJ Digit Med 3:119. https://doi.org/10.1038/s41746-020-00323-1. (PMID: 10.1038/s41746-020-00323-1330153727490367)
European Union. European Health Data Space. https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en . Accessed 18 Jan 2024.
Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206–215. https://doi.org/10.1038/s42256-019-0048-x. (PMID: 10.1038/s42256-019-0048-x356030109122117)
Ghassemi M, Oakden-Rayner L, Beam AL (2021) The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 3(11):e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9. (PMID: 10.1016/S2589-7500(21)00208-934711379)
Jaworski BK et al (2023) Advancing digital health equity: directions for behavioral and social science research. Transl Behav Med 13(3):132–139. https://doi.org/10.1093/tbm/ibac088. (PMID: 10.1093/tbm/ibac08836318232)
UNESCO, Recommendations on the Ethics of Artificial Intelligence, 3 November 2021. [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000381137 . Accessed 20 Jan 2024.
World Health Organization, Ethics and governance of artificial intelligence for health: guidance on large multi-modal models, 2024. [Online]. Available: https://iris.who.int/handle/10665/375579 . Accessed 20 Jan 2024.
White TL, Gonsalves MA (2021) Dignity neuroscience: universal rights are rooted in human brain science. Ann NY Acad Sci 1505(1):40–54. https://doi.org/10.1111/nyas.14670. (PMID: 10.1111/nyas.1467034350987)
معلومات مُعتمدة: (StARR) Program (R38) Award 5R38-CA245204 NIH/NCI
فهرسة مساهمة: Keywords: Artificial intelligence; Brain health; Digital health; Future trends; Machine learning; Neurology; Policy
تواريخ الأحداث: Date Created: 20240217 Date Completed: 20240427 Latest Revision: 20240705
رمز التحديث: 20240706
DOI: 10.1007/s00415-024-12220-8
PMID: 38367046
قاعدة البيانات: MEDLINE
الوصف
تدمد:1432-1459
DOI:10.1007/s00415-024-12220-8