An Overview of Artificial Intelligence in Neurology

Abstract

The convergence of artificial intelligence (AI) and neuroscience represents one of medicine's most profound intellectual partnerships. Neuronal architecture has inspired computational methods, while computational models, evolving from theoretical constructs to transformative clinical tools, are reshaping neurological practice. As AI systems attempt to augment diagnostic accuracy, treatment planning, and patient care, neurologists must develop fluency in these technologies to harness their potential while navigating their limitations and dangers. AI-related publications have exponentially increased in recent years, yet many neurologists lack the foundational computer science background needed to critically evaluate and most safely and effectively implement these tools in clinical practice. This article serves to outline the historical foundations linking neuroscience to computing, examine core concepts of the past and current AI landscape in neurology, and describe methodologies that aim to revolutionize neurological care.

Keywords

artificial intelligence - computer vision - generative AI

Publication History

Received: 20 August 2025

Accepted: 30 August 2025

Accepted Manuscript online:
01 September 2025

Article published online:
14 October 2025

• References

• 1 Kalani M, Anjankar A. Revolutionizing neurology: The role of artificial intelligence in advancing diagnosis and treatment. Cureus 2024; 16 (06) e61706
• 2 Zulman DM, Shah NH, Verghese A. Evolutionary pressures on the electronic health record: Caring for complexity. JAMA 2016; 316 (09) 923-924
• 3 Bösel J, Mathur R, Cheng L, Varelas MS, Hobert MA, Suarez JIAI. AI and neurology. Neurol Res Pract 2025; 7 (01) 11
• 4 Komorowski M, Celi LA. Will artificial intelligence contribute to overuse in healthcare?. Crit Care Med 2017; 45 (05) 912-913
• 5 Hadar PN, Moura LMVR. Clinical applications of artificial intelligence in neurology practice. Continuum (Minneap Minn) 2025; 31 (02) 583-600
• 6 Chaddad A, Peng J, Xu J, Bouridane A. Survey of explainable AI techniques in healthcare. Sensors (Basel) 2023; 23 (02) 634
• 7 London AJ. Artificial intelligence and black-box medical decisions: Accuracy versus explainability. Hastings Cent Rep 2019; 49 (01) 15-21
• 8 Linardatos P, Papastefanopoulos V, Kotsiantis S, Explainable AI. Explainable AI: A review of machine learning interpretability methods. Entropy (Basel) 2020; 23 (01) 18
• 9 Marcus E, Teuwen J. Artificial intelligence and explanation: How, why, and when to explain black boxes. Eur J Radiol 2024; 173: 111393
• 10 Chan B. Black-box assisted medical decisions: AI power vs. ethical physician care. Med Health Care Philos 2023; 26 (03) 285-292
• 11 Moura L, Jones DT, Sheikh IS. et al. Implications of large language models for quality and efficiency of neurologic care: Emerging issues in neurology. Neurology 2024; 102 (11) e209497
• 12 Fogo AB, Kronbichler A, Bajema IMA. AI's threat to the medical profession. JAMA 2024; 331 (06) 471-472
• 13 Zador A, Escola S, Richards B. et al. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nat Commun 2023; 14 (01) 1597
• 14 Naveed H, Khan AU, Qiu S. et al. A comprehensive overview of large language models. Published online October 17 2024; . arXiV:2307.06435
• 15 Voss P, Jovanovic M. Why we don't have AGI yet. Published online September 19 2023; . arXiV:2308.03598
• 16 Amaro Junior E. Artificial intelligence and Big Data in neurology. Arq Neuropsiquiatr 2022; 80 (5 Suppl 1): 342-347
• 17 Michaud EJ, Parker-Sartori A, Tegmark M. On the creation of narrow AI: Hierarchy and nonlocality of neural network skills. Published online May 21 2025; . arXiV:2505.15811
• 18 Turing AM. Computing machinery and intelligence. Mind 1950; 59 (236) 433-460
• 19 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521 (7553) 436-444
• 20 Vaswani A, Shazeer N, Parmar N. et al. Attention is all you need. Published online 2017; . arXiV:1706.03762
• 21 Brown TB, Mann B, Ryder N. et al. Language models are few-shot learners. Published online 2020; . arXiV:2005.14165
• 22 Suenghataiphorn T, Tribuddharat N, Danpanichkul P, Kulthamrongsri N. Bias in large language models across clinical applications: A systematic review. Published online 2025; . arXiV:2504.02917
• 23 Guo Y, Guo M, Su J. et al. Bias in large language models: Origin, evaluation, and mitigation. Published online November 16 2024; . arXiV:2411.10915
• 24 Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018; 178 (11) 1544-1547
• 25 Huang L, Yu W, Ma W. et al. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Trans Inf Syst 2025; 43 (02) 1-55
• 26 Roustan D, Bastardot F. The clinicians' guide to large language models: A general perspective with a focus on hallucinations. Interact J Med Res 2025; 14 (01) e59823
• 27 Xu Z, Jain S, Kankanhalli M. Hallucination is inevitable: An innate limitation of large language models. Published online February 13 2025; . arXiV:2401.11817
• 28 Chai J, Zeng H, Li A, Ngai EWT. Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Mach Learn Appl 2021; 6: 100134
• 29 Olveres J, González G, Torres F. et al. What is new in computer vision and artificial intelligence in medical image analysis applications?. Quant Imaging Med Surg 2021; 11 (08) 3830-3853
• 30 El-Assy AM, Amer HM, Ibrahim HM, Mohamed MA. A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data. Sci Rep 2024; 14 (01) 3463
• 31 Karamchandani RR, Helms AM, Satyanarayana S. et al. Automated detection of intracranial large vessel occlusions using Viz.ai software: Experience in a large, integrated stroke network. Brain Behav 2023; 13 (01) e2808
• 32 Rauschecker AM, Rudie JD, Xie L. et al. Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI. Radiology 2020; 295 (03) 626-637
• 33 Neumann JV. The Computer and The Brain. Yale University Press; 1958
• 34 McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943; 5 (04) 115-133
• 35 Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958; 65 (06) 386-408
• 36 Rumelhart DE, Hintont GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986; 323: 533-536
• 37 Elharrouss O, Mahmood Y, Bechqito Y. et al. Loss functions in deep learning: A comprehensive review. Published online April 5 2025; . arXiV:2504.04242
• 38 Macdonald N. Language translation by machine—a report of the first successful trial. Comput Autom 1954; 3 (02) 10-13
• 39 Shortliffe EH, Buchanan BG. A model of inexact reasoning in medicine. Math Biosci 1975; 23 (3–4): 351-379
• 40 Miller RA, Pople HE, Myers JD. INTERNIST-I, An experimental computer-based diagnostic consultant for general internal medicine. In: Reggia JA, Tuhrim S. eds. Computer-Assisted Medical Decision Making. New York: Springer; 1985. :pp. 139-158
• 41 Banks G. Artificial intelligence in medical diagnosis: the INTERNIST/CADUCEUS approach. Crit Rev Med Inform 1986; 1 (01) 23-54
• 42 Miller RA, Masarie FE. Quick Medical Reference (QMR): A microcomputer-based diagnostic decision-support system for general internal medicine. Proc Annu Symp Comput Appl Med Care 1990; 986-988
• 43 Harris ZS. Distributional structure. Word 1954; 10 (2–3): 146-162
• 44 Salton G. Some experiments in the generation of word and document associations. Paper presented at: Proceedings of the December 4–6, 1962, Fall Joint Computer Conference on - AFIPS '62 (Fall). ACM Press 1962 :pp. 234–250
• 45 Salton G, Wong A, Yang CS. A vector space model for automatic indexing. Commun ACM 1975; 18 (11) 613-620
• 46 Knox S, Aghamoosa S, Heider PM. et al. AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records. Alzheimers Dement (N Y) 2025; 11 (02) e70089
• 47 Hao X, Abeysinghe R, Zheng F, Schulz PE, Cui L. Alzheimer's Disease Neuroimaging Initiative. Mapping of Alzheimer's disease related data elements and the NIH common data elements. BMC Med Inform Decis Mak 2024; 24 (Suppl. 03) 103
• 48 Zhao Y, Ren B, Yu W. et al. Construction of an assisted model based on natural language processing for automatic early diagnosis of autoimmune encephalitis. Neurol Ther 2022; 11 (03) 1117-1134
• 49 Nielsen FÅ, Balslev D, Hansen LK. Mining the posterior cingulate: segregation between memory and pain components. Neuroimage 2005; 27 (03) 520-532
• 50 Pennington J, Socher R, Manning C. Glove: Global Vectors for Word Representation. Paper presented at: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics 2014 :pp. 1532-1543
• 51 Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. Published online 2013; . arXiV:1301.3781
• 52 Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. Published online 2013; . arXiV:1310.4546
• 53 Pakhale K. Comprehensive overview of named entity recognition: Models, domain-specific applications and challenges. Published online 2023; . arXiV:2309.14084
• 54 Beltagy I, Lo K, Cohan A. SciBERT: A Pretrained Language Model for Scientific Text. Paper presented at: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics 2019 :pp. 3613-3618
• 55 Lee J, Yoon W, Kim S. et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2020; 36 (04) 1234-1240
• 56 Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med 2021; 4 (01) 86
• 57 Huang K, Altosaar J, Ranganath R. ClinicalBERT: Modeling clinical notes and predicting hospital readmission. Published online 2019; . arXiV:1904.05342
• 58 Mosbach M, Andriushchenko M, Klakow D. On the stability of fine-tuning BERT: Misconceptions, explanations, and strong baselines. Published online 2020; . arXiV:2006.04884
• 59 McCoy T, Pavlick E, Linzen T. Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. Paper presented at: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2019 :pp. 3428-3448
• 60 Niven T, Kao HY. Probing Neural Network Comprehension of Natural Language Arguments. Paper presented at: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2019 :pp. 4658-4664
• 61 Vakili T, Henriksson A, Dalianis H. End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility. BMC Med Inform Decis Mak 2024; 24 (01) 162
• 62 Nasr M, Shokri R, Houmansadr A. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. Paper presented at: 2019 IEEE Symposium on Security and Privacy (SP). IEEE; 2019 :pp. 739-753
• 63 Mireshghallah F, Goyal K, Uniyal A, Berg-Kirkpatrick T, Shokri R. Quantifying privacy risks of masked language models using membership inference Attacks. Published online 2022; . arXiV:2203.03929
• 64 Diera A, Lell N, Garifullina A, Scherp A. Memorization of Named Entities in Fine-Tuned BERT Models. Paper presented at: Holzinger A, Kieseberg P, Cabitza F, Campagner A, Tjoa AM, Weippl E. eds. Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science. Springer Nature Switzerland; 2023. :Vol. 14065: pp. 258-279
• 65 Rogers A, Kovaleva O, Rumshisky A. A Primer in BERTology: What we know about how BERT works. Published online November 9, 2020; . arXiV:2002.12327
• 66 Strubell E, Ganesh A, McCallum A. Energy and policy considerations for deep learning in NLP. Published online June 5, 2019; . arXiV:1906.02243
• 67 Radford A, Narasimhan K, Salimans T, Sutskever I. Improving Language Understanding by Generative Pre-Training. OpenAI; 2018 . Accessed September 3, 2025 at: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
• 68 Zheng Z, Wang Y, Huang Y. et al. Attention heads of large language models. Patterns (N Y) 2025; 6 (02) 101176
• 69 Qi X, Zeng Y, Xie T. et al. Fine-tuning aligned language models compromises safety, even when users do not intend to!. Published online 2023; . arXiV:2310.03693
• 70 Zheng Q, Liu H, Zhang X. et al. Machine memory intelligence: Inspired by human memory mechanisms. Engineering 2025; (E-pub ahead of print)
• 71 Whittington JCR, Warren J, Behrens TEJ. Relating transformers to models and neural representations of the hippocampal formation. Published online 2021; . arXiV:2112.04035
• 72 Kim DK, Kwon J, Cha M, Lee C. Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity. Paper presented at: Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. 37th Conference on Neural Information Processing Systems (NeurIPS 2023); 2023 :Vol. 36: pp. 14637-14664 . Accessed September 3, 2025 at: https://proceedings.neurips.cc/paper_files/paper/2023/file/2f1eb4c897e63870eee9a0a0f7a10332-Paper-Conference.pdf
• 73 Wang L, Zhang X, Li Q. et al. Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Nat Mach Intell 2023; 5 (12) 1356-1368
• 74 Hickok G. The functional neuroanatomy of language. Phys Life Rev 2009; 6 (03) 121-143
• 75 Asgari E, Montaña-Brown N, Dubois M. et al. A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation. NPJ Digit Med 2025; 8 (01) 274
• 76 Muralidharan V, Adewale BA, Huang CJ. et al. A scoping review of reporting gaps in FDA-approved AI medical devices. NPJ Digit Med 2024; 7 (01) 273
• 77 Hager P, Jungmann F, Holland R. et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat Med 2024; 30 (09) 2613-2622
• 78 Alowais SA, Alghamdi SS, Alsuhebany N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 2023; 23 (01) 689
• 79 Carlini N, Tramer F, Wallace E. et al. Extracting training data from large language models. Published online June 15, 2021; . arXiV:2012.07805
• 80 Yang Y, Liu X, Jin Q, Huang F, Lu Z. Unmasking and quantifying racial bias of large language models in medical report generation. Published online January 25, 2024 . arXiv:2401.13867v1
• 81 Kim Y, Jeong H, Chen S. et al. Medical hallucination in foundation models and their impact on healthcare. Published online March 3, 2025; . medRxiv 2025.02.28.25323115
• 82 Singhal K, Azizi S, Tu T. et al. Large language models encode clinical knowledge. Nature 2023; 620 (7972) 172-180
• 83 Schubert MC, Wick W, Venkataramani V. Performance of large language models on a neurology board-style examination. JAMA Netw Open 2023; 6 (12) e2346721
• 84 Kim JW, Alaa A, Bernardo D. EEG-GPT: Exploring capabilities of large language models for EEG classification and interpretation. Published online February 3, 2024; . arXiv:2401.18006
• 85 Li CY, Chang KJ, Yang CF. et al. Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation. Nat Commun 2025; 16 (01) 2258
• 86 Yao Z, Wang Z, Xie W. et al. Applications of generative artificial intelligence in brain MRI image analysis for brain disease diagnosis. Neuropharmacol Ther 2024; 1: 65-77
• 87 Tang X, Dai H, Knight E. et al. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. Brief Bioinform 2024; 25 (04) bbae338
• 88 Gangwal A, Ansari A, Ahmad I. et al. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15: 1331062
• 89 Li J, Cairns BJ, Li J, Zhu T. Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications. NPJ Digit Med 2023; 6 (01) 98
• 90 Barr AA, Quan J, Guo E, Sezgin E. Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data. Front Artif Intell 2025; 8: 1533508
• 91 Wahlig SG, Nedelec P, Weiss DA, Rudie JD, Sugrue LP, Rauschecker AM. 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies. Front Neurosci 2023; 17: 1188336
• 92 Ahmedt-Aristizabal D, Armin MA, Hayder Z. et al. Deep learning approaches for seizure video analysis: A review. Epilepsy Behav 2024; 154: 109735
• 93 LeCun Y, Boser B, Denker JS. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput 1989; 1 (04) 541-551
• 94 Matsoukas S, Morey J, Lock G. et al. AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study. J Neurointerv Surg 2023; 15 (01) 52-56
• 95 Dosovitskiy A, Beyer L, Kolesnikov A. et al. An image is worth 16 × 16 words: Transformers for image recognition at scale. Published online June 3, 2021; . arXiv:2010.11929
• 96 Mubonanyikuzo V, Yan H, Komolafe TE, Zhou L, Wu T, Wang N. Detection of Alzheimer disease in neuroimages using vision transformers: Systematic review and meta-analysis. J Med Internet Res 2025; 27: e62647
• 97 Mayfield JD, Murtagh R, Ciotti J, Robertson D, Naqa IE. Time-dependent deep learning prediction of multiple sclerosis disability. J Imaging Inform Med 2024; 37 (06) 3231-3249
• 98 Bordes F, Pang RY, Ajay A. et al. An introduction to vision-language modeling. Published online May 27, 2024; . arXiv:2405.17247
• 99 Hartsock I, Rasool G. Vision-language models for medical report generation and visual question answering: A review. Front Artif Intell 2024; 7: 1430984
• 100 Radford A, Kim JW, Hallacy C. et al. Learning transferable visual models from natural language supervision. Published online February 26, 2021; . arXiv:2103.00020
• 101 Lyu Y, Harake S, Chowdury A. et al. Learning neuroimaging models from health system-scale data. Res Sq 2025; rs.3.rs-5932803
• 102 Yuan Z, Jin Q, Tan C. et al. RAMM: Retrieval-augmented biomedical visual question answering with multi-modal pre-training. Published online 2023; . arXiv:2303.00534
• 103 Lu MY, Chen B, Williamson DFK. et al. A multimodal generative AI copilot for human pathology. Nature 2024; 634 (8033) 466-473
• 104 AbuAlrob MA, Mesraoua B. Harnessing artificial intelligence for the diagnosis and treatment of neurological emergencies: a comprehensive review of recent advances and future directions. Front Neurol 2024; 15: 1485799
• 105 Wang HE, Triebkorn P, Breyton M. et al. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11 (05) nwae079
• 106 AbuAlrob MA, Itbaisha A, Mesraoua B. Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine. Epilepsy Behav 2025; 166: 110327
• 107 Pedersen M, Verspoor K, Jenkinson M, Law M, Abbott DF, Jackson GD. Artificial intelligence for clinical decision support in neurology. Brain Commun 2020; 2 (02) fcaa096
• 108 Littlejohn KT, Cho CJ, Liu JR. et al. A streaming brain-to-voice neuroprosthesis to restore naturalistic communication. Nat Neurosci 2025; 28 (04) 902-912
• 109 Awuah WA, Ahluwalia A, Darko K. et al. Bridging minds and machines: The recent advances of brain-computer interfaces in neurological and neurosurgical applications. World Neurosurg 2024; 189: 138-153
• 110 Schuman CD, Kulkarni SR, Parsa M, Mitchell JP, Date P, Kay B. Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci 2022; 2 (01) 10-19
• 111 Aboumerhi K, Güemes A, Liu H, Tenore F, Etienne-Cummings R. Neuromorphic applications in medicine. J Neural Eng 2023; 20 (04) 046042
• 112 Colelough BC, Regli W. Neuro-symbolic AI in 2024: A systematic review. Published online April 5, 2025; . arXiv:2501.05435
• 113 Tveit J, Aurlien H, Plis S. et al. Automated interpretation of clinical electroencephalograms using artificial intelligence. JAMA Neurol 2023; 80 (08) 805-812
• 114 Albers GW, Wald MJ, Mlynash M. et al. Automated calculation of Alberta Stroke Program Early CT Score: Validation in patients with large hemispheric infarct. Stroke 2019; 50 (11) 3277-3279
• 115 Gao Y, Li R, Croxford E. et al. Leveraging medical knowledge graphs into large language models for diagnosis prediction: Design and application study. JMIR AI 2025; 4: e58670
• 116 Kattih M, Bressler M, Smith LR, Schinelli A, Mhaskar R, Hanna K. Artificial intelligence-prompted explanations of common primary care diagnoses. PRiMER Peer-Rev Rep Med Educ Res 2024; 8: 51
• 117 Badreddine S, Garcez Ad'Avila, Serafini L, Spranger M. Logic tensor networks. Artif Intell 2022; 303: 103649
• 118 Jones DT, Kerber KA. Artificial intelligence and the practice of neurology in 2035: The Neurology Future Forecasting Series. Neurology 2022; 98 (06) 238-245