REGISTRO DOI: 10.69849/revistaft/ni10202510240653
Alan Bessa Aguiar; Ianca Macêdo Costa; Maria Camila Timbó Rocha; Letícia Medeiros Biana Lessa; Maria Eduarda Araujo de Freitas; Marjory Mont Alverne Bezerra; Virna Almeida Coutinho; Júlia Torres Rodrigues Costa; Kaylanne Silva Lima; Francisco Machado Junior; Caio César Otôni Espíndola Rocha
Abstract
Artificial intelligence (AI) has promoted significant advances in neurology, an area marked by diagnostic and therapeutic challenges due to the complexity of the nervous system. The objective of this study was to synthesize the current evidence on the applications of AI in neurological practice. This is a descriptive review of the literature in the Medline databases, including articles from 2015 to 2025. Initially, 710 articles were found, of which 60 were selected after applying inclusion and exclusion criteria. The studies analyzed show that the technology contributes to the early detection of disorders such as Alzheimer’s, Parkinson’s, epilepsy, and stroke by identifying subtle patterns in clinical, genomic, and imaging data that often go unnoticed by specialists. In neuroimaging analysis, AI improves the identification and characterization of lesions, in addition to optimizing the acquisition and quality of exams, reducing time, costs, and radiation exposure. Precision medicine is also highlighted, in which personalized algorithms favor individualized therapies, more accurate prognoses, and greater patient adherence. Furthermore, AI-integrated brain-computer interfaces represent an innovation for rehabilitation and functional assistance for individuals with severe neurological impairments. Despite promising results, challenges remain related to database standardization, robust clinical validation, and ethical issues involving transparency and accountability in the use of these technologies. It is concluded that AI ushers in a new era in neurology, with the potential to transform diagnostic, therapeutic, and rehabilitative care in an ethical and patient-centered manner.
Keywords: Artificial intelligence; Neurology; Advances in neurology; Technology.
Resumo
A inteligência artificial (IA) tem promovido avanços significativos na neurologia, área marcada por desafios diagnósticos e terapêuticos devido à complexidade do sistema nervoso. O objetivo do estudo foi sintetizar as evidências atuais das aplicações da IA na prática neurológica. Trata-se de uma revisão descritiva da literatura nas bases de dados Medline, com inclusão de artigos de 2015 a 2025. Foram encontrados inicialmente 710 artigos, dos quais 60 foram selecionados após aplicação de critérios de inclusão e exclusão. Os estudos analisados mostram que a tecnologia contribui para a detecção precoce de distúrbios como Alzheimer, Parkinson, epilepsia e acidente vascular cerebral, ao identificar padrões sutis em dados clínicos, genômicos e de imagem que muitas vezes passam despercebidos por especialistas. Na análise de neuroimagem, a IA aprimora a identificação e caracterização de lesões, além de otimizar a aquisição e qualidade dos exames, reduzindo tempo, custos e exposição a radiação. A medicina de precisão também recebe destaque, na qual algoritmos personalizados favorecem terapias individualizadas, prognósticos mais acurados e maior adesão dos pacientes. Ademais, as interfaces cérebro-computador integradas à IA representam uma inovação para reabilitação e assistência funcional de indivíduos com comprometimentos neurológicos graves. Apesar dos resultados promissores, permanecem desafios relacionados à padronização de bases de dados, validação clínica robusta e questões éticas envolvendo transparência e responsabilidade no uso dessas tecnologias. Conclui-se que a IA inaugura uma nova era na neurologia, com potencial para transformar cuidados diagnósticos, terapêuticos e reabilitadores de forma ética e centrada no paciente.
Palavras-chave: Inteligência artificial; Neurologia; Avanços em neurologia; Tecnologia.
1. Introduction
Recently, Artificial Intelligence (AI) technologies have generated significant transformations in the healthcare sector, sparking an active debate about how broadly these systems can support healthcare professionals in improving clinical decision-making, with the possibility of even replacing human intervention in specific areas of medical care (Jiang et al., 2017). The increased availability of health-related data, coupled with rapid advances in techniques for analyzing large volumes of information, has enabled the successful implementation of AI solutions in this area. Guided by relevant clinical questions, powerful AI tools have the ability to identify clinically relevant information that is hidden in vast sets of data, thus contributing to the improvement of decision-making in medical practice across different specialties (Murdoch et al., 2013; Kolker et al., 2016; Jha et al., 2016).
In recent years, the field of neurology has witnessed a remarkable transformation due to the integration of artificial intelligence. AI, with its focus on creating computer algorithms that mimic human cognitive abilities such as learning, reasoning, and self-correction, is revolutionizing the understanding, diagnosis, and treatment of neurological disorders (Patel et al., 2021; Basu et al., 2020). As AI technologies evolve and mature, their potential to revolutionize healthcare in neurology becomes increasingly evident, given that, with their ability to process large amounts of data, recognize patterns, and generate accurate predictions, countless benefits—once considered unattainable—are becoming a concrete reality (Ahuja et al., 2019; Li et al., 2023; Bohr et al., 2020).
Nervous system disorders, with their complex manifestations, pose considerable challenges (Chin et al., 2014), but the integration of AI into neurology has yielded promising results, such as early disease detection, optimization of more individualized treatment plans, improved neuroimaging analysis, genetic information processing, and advances in scientific research (Tripathi et al., 2024; Gautam et al., 2020; Singh et al., 2023), with emphasis on brain-computer interfaces (BCIs), which, powered by artificial intelligence, provide innovative interactions for patients with severe neurological impairment (Peksa et al., 2023; Olsen et al., 2021). These advances are not limited to the technological aspect, but also have the potential to transform the lives of individuals affected by neurological disorders, providing greater precision, efficiency, and substantial improvement in quality of life (Harry et al., 2023).
According to the information presented, and given the great advances that powerful artificial intelligence tools have been making, the present study aims to gather updated information on the spectrum of these technologies in the context of neurology, in order to elucidate how AI can revolutionize the care of neurological disorders.
2. Methodology
A narrative literature review was conducted in the Medline Databases, from 2015 to 2025, on artificial intelligence in neurology. In the Medline, 710 articles were found by the Medical Subject Headings (MeSH) descriptor “(artificial intelligence) AND (neurology) AND (advancement in neurology) AND (technology)”, of which 241 were selected, after the first set of criteria — exclusion of titles that discussed either AI or neurology but not both, articles not included in the search period 2015-2025, as well as non- English articles. The second set of criteria — exclusion of the abstracts not addressed to artificial intelligence in neurology, as well as articles with animal models — was applied, by which 185 articles were excluded. To ensure content saturation, the authors reviewed the included research references and related reviews on the topics to identify any missing publications. Furthermore, 4 articles from the Medline Database were manually screened and added according to their relevance in the qualitative evidence synthesis. Of the total, 60 original articles remained (Figure 1).
Figure 1 – Flowchart of the article selection.
3. Results and discussion
3.1 Early detection
Early diagnosis of neurological disorders is essential due to the progressive and sometimes irreversible nature of these conditions, but it remains a challenge because of the subtlety with which symptoms often present (Singh et al., 2023). Artificial intelligence has emerged as a promising tool in this context, processing large volumes of clinical data, such as medical histories, imaging exams, and genetic information, to identify early markers that may go unnoticed by human specialists (Surianarayanan et al., 2023; Hosny et al., 2018; Kumar et al., 2023). In diseases such as Alzheimer’s and Parkinson’s, for example, AI is capable of detecting changes before symptoms manifest, enabling interventions in the early stages (Vrahatis et al., 2023).
Early detection of neurological conditions, such as cerebral aneurysms, strokes, and epileptic seizures, is essential for effective interventions and the prevention of serious clinical outcomes (Chan et al., 2019; Kamal et al., 2018; Bruni et al., 2017). In this scenario, artificial intelligence-based tools present themselves as promising alternatives since, in contrast to conventional methods, which often depend on subjective interpretations by professionals, AI systems promote greater objectivity in the analysis of clinical data (Ienca et al., 2020; Chekroud et al., 2016). This approach contributes to the optimization of resources, reduction of response time, and improvement of the decision-making process in medical practice.
3.2 Neuroimaging analysis
Neuroimaging modalities are divided into structural and functional techniques, with complementary functions in brain assessment. Structural techniques, such as magnetic resonance imaging (MRI) and computed tomography (CT), provide detailed images of the anatomy and morphology of the brain (Fox et al., 2018; Liu et al., 2023), while functional techniques, such as positron emission tomography (PET), single photon emission computed tomography (SPECT), among others, analyze physiological processes, including metabolism, perfusion, and brain electrical activity (Liu et al., 2023; Mier et al., 2015; Wintermark et al., 2018). Furthermore, the multimodal combination of these techniques enhances the understanding of the structural and functional organization of the brain (Mier et al., 2015).
In this context, artificial intelligence, with its different techniques, has been widely used in neuroimaging analysis and can be applied to a diverse range of tasks faced by radiologists (Surianarayanan et al., 2023), with the advantage of making the process more accurate, objective, and efficient, given its automation (Zhu et al., 2019). Using computer vision strategies, AI is capable of identifying and segmenting anatomical structures, detecting various types of lesions, such as microhemorrhages, ischemic areas, lacunae, metastases, aneurysms, primary brain tumors, and white matter hyperdensities (Savadjiev et al., 2019; Zaharchuk et al., 2018; McBee et al., 2018).
In addition to these tasks more related to the study of the image itself, such as detection, characterization, and diagnosis of abnormalities, deep neural networks of AI are also used to perform different tasks particularly applied to image acquisition, ranging from artifact removal, quality improvement, reconstruction, and image normalization to even reducing radiation and contrast doses and shortening exam times (McBee et al., 2018; Lin et al., 2021; Zhu et al., 2019).
3.3 Precision medicine
The application of artificial intelligence is transforming therapeutic approaches in neurology, providing a more accurate and personalized analysis for each patient, thus contributing to precision medicine (Balboni et al., 2022; Kocher et al., 2020). AI offers the possibility of creating treatment plans based on each patient’s unique genetic characteristics and medical history, reducing trial and error in predicting patient response to medications or therapies (Johnson et al., 2021). As an example, these technologies can identify mutations in brain tumors, guiding the selection of specific regimens and improving therapeutic response (Char et al., 2018).
Based on accumulated data from previous patients, AI is also capable of anticipating the progression of neurological diseases and minimizing the risk of complications (Oettl et al., 2024), indicating the adjustment of therapies earlier by identifying biomarkers and patterns indicative of the progression of the disorder, in addition to offering more accurate prognoses for patients and their families (Char et al., 2018; Giger et al., 2018).
AI also has the ability to analyze drug interactions and predict potential adverse events, helping clinicians make informed decisions about medication choices (Shaik et al., 2023; Snoswell et al., 2021). The development of applications or other devices can be used to help patients monitor the side effects of drugs in real time, with artificial intelligence analyzing this data and providing insights to patients and healthcare services (Segato et al., 2020; Jadczyk et al., 2021).
In addition, different tools can be used to educate patients about their health conditions, treatment options, and lifestyle changes that would benefit them most, making them active agents in the health-disease process and thus contributing to greater adherence to the therapeutic proposal (Kalani et al., 2024).
3.4 BCIs
The brain-computer interface enables brain signals to be captured and converted into commands capable of operating external devices, such as robotic prostheses, personal computers, voice synthesizers, electronic wheelchairs, among others (Wang et al., 2024; Ienca et al., 2020; Daly et al., 2008). The goal is to restore functionality in individuals with neuromuscular disorders, including stroke, amyotrophic lateral sclerosis, spinal cord injuries, and cerebral palsy (Shih et al., 2012; Slutzky et al., 2019).
Typically, signals are obtained by semi-invasive or non-invasive techniques, such as electroencephalography (EEG), and, after undergoing different processing stages, the commands to be sent to the devices are determined, allowing, for example, paralyzed people to perform movements with robotic arms that replace missing parts of their body (Surianarayanan et al., 2023). Recent studies address in detail several applications of AI-based BCIs, highlighting their potential in rehabilitation and functional assistance (Zhang et al., 2020; Slutzky et al., 2019).
AI software is essential to enable this direct, bidirectional brain-computer communication, as it allows, on the one hand, the translation of neural activity into machine code to control external entities and, on the other hand, the anticipation of brain responses to sensory stimuli, enabling the accurate transmission of sensations to humans (Ienca et al., 2020; Silva et al., 2018). Furthermore, state-of-the-art BCIs operate in a closed loop, automatically adjusting their parameters through integrated AI algorithms, which favors faster and more effective adaptation to patients’ clinical demands (Remsik et al., 2016; Lee et al., 2019).
3.5 Future challenges
The incorporation of artificial intelligence into neurological practice is significantly promising, but faces significant challenges (McCradden et al., 2020). Among these, the need to gather extensive, consistent, and high-quality databases capable of supporting the training and validation of AI models with methodological rigor stands out (Kocher et al., 2020; Broggi et al., 2024). Ethical issues also play a central role, requiring strategies that ensure informed consent from patients, transparency of algorithmic processes, and responsible use of these technologies (Topol et al., 2019; Char et al., 2018; Vayena et al., 2018).
Robust clinical studies under real-world conditions are necessary to confirm the efficacy and safety of these tools and to ensure that the results provided by artificial intelligence are interpretable and clinically meaningful (Broggi et al., 2024; Lee et al., 2021). Encouraging patient engagement so that they can understand the process and thus place their trust in it is also a necessary condition (Gerke et al., 2020). Above all, it is essential that the recommendations and interventions proposed by AI prioritize results that ensure the well-being and comfort of patients (Davenport et al., 2019).
Finally, it is understood that these and several other challenges exist, but they must be progressively overcome with continuous research collaboration and the ethical and responsible use of these technologies (Kalani et al., 2024).
4. Conclusion
The incorporation of artificial intelligence into neurology ushers in a new era, marked not only by technological advances but also by profound reflections on the future of healthcare. AI expands the frontiers of what is possible in the care of people with neurological disorders, since, by enabling the analysis of large volumes of clinical data and the identification of complex patterns, it favors earlier diagnoses, personalized interventions, and rehabilitation resources that were previously unimaginable. However, although the advances are promising, significant challenges still need to be addressed, including ethical issues, regulatory requirements, the need for robust validation in real clinical settings, and ensuring algorithmic transparency. Progressively overcoming these barriers will depend on collaborative efforts between researchers, healthcare professionals, technology developers, and public policy makers. Thus, the revolutionary potential of artificial intelligence in neurology is likely to be fully realized through ethical, safe, and patient-centered implementation that ensures tangible and sustainable benefits in healthcare practice and in the lives of individuals affected by neurological diseases.
References
Ahuja, A.S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7, e7702. https://doi.org/10.7717/peerj.7702
Balboni, E., Nocetti, L., Carbone, C., Dinsdale, N., Genovese, M., Guidi, G., Malagoli, M., Chiari, A., Namburete, A. I. L., Jenkinson, M., & Zamboni, G. (2022). The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects. Human brain mapping, 43(11), 3427–3438. https://doi.org/10.1002/hbm.25858
Basu, K., Sinha, R., Ong, A., & Basu, T. (2020). Artificial Intelligence: How is It Changing Medical Sciences and Its Future?. Indian journal of dermatology, 65(5), 365–370. https://doi.org/10.4103/ijd.IJD_421_20
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Broggi, G., Mazzucchelli, M., Salzano, S., Barbagallo, G. M. V., Certo, F., Zanelli, M., Palicelli, A., Zizzo, M., Koufopoulos, N., Magro, G., & Caltabiano, R. (2024). The emerging role of artificial intelligence in neuropathology: Where are we and where do we want to go?. Pathology, research and practice, 263, 155671. https://doi.org/10.1016/j.prp.2024.155671
Bruni, A., Martino, I., Borzì, G., Gambardella, A., De Fazio, P., & Labate, A. (2017). The mystery of unexplained traumatic sudden falls. A clinical case that adds a new feasible cause. Neurol sci, 38(6), 1115–1117. https://doi.org/10.1007/s10072-017-2843-7
Chan, K. L., Leng, X., Zhang, W., Dong, W., Qiu, Q., Yang, J., Soo, Y., Wong, K. S., Leung, T. W., & Liu, J. (2019). Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network. Frontiers in neurology, 10, 171. https://doi.org/10.3389/fneur.2019.00171
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing Machine Learning in Health Care – Addressing Ethical Challenges. The New England journal of medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: a machine learning approach. The lancet. Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-X
Chin, J. H., & Vora, N. (2014). The global burden of neurologic diseases. Neurology, 83(4), 349–351. https://doi.org/10.1212/WNL.0000000000000610
Daly, J. J., & Wolpaw, J. R. (2008). Brain-computer interfaces in neurological rehabilitation. The Lancet. Neurology, 7(11), 1032–1043. https://doi.org/10.1016/S1474-4422(08)70223-0
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Fox M. D. (2018). Mapping Symptoms to Brain Networks with the Human Connectome. The New England journal of medicine, 379(23), 2237–2245. https://doi.org/10.1056/NEJMra1706158koh
Gautam, R., & Sharma, M. (2020). Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis. Journal of medical systems, 44(2), 49. https://doi.org/10.1007/s10916-019-1519-7
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Giger, M. L. (2018). Machine Learning in Medical Imaging. J. Am. Coll. Radio., 15, 512–520. https://doi.org/10.1016/j.jacr.2017.12.028
Harry, A. (2023). The future of medicine: harnessing the power of AI for revolutionizing healthcare. Int J Multidisip Res, 2, 36-47. https://doi.org/10.47709/ijmdsa.v2i1.2395
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature reviews. Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
Ienca, M., & Ignatiadis, K. (2020). Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges. AJOB neuroscience, 11(2), 77–87. https://doi.org/10.1080/21507740.2020.1740352
Jadczyk, T., Wojakowski, W., Tendera, M., Henry, T. D., Egnaczyk, G., & Shreenivas, S. (2021). Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology. Journal of medical Internet research, 23(5), e22959. https://doi.org/10.2196/22959
Jha, S., & Topol, E. J. (2016). Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists. JAMA, 316(22), 2353–2354. https://doi.org/10.1001/jama.2016.17438
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and translational science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
Kalani, M., & Anjankar, A. (2024). Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment. Cureus, 16(6), e61706. https://doi.org/10.7759/cureus.61706
Kamal, H., Lopez, V., & Sheth, S. A. (2018). Machine Learning in Acute Ischemic Stroke Neuroimaging. Frontiers in neurology, 9, 945. https://doi.org/10.3389/fneur.2018.00945
Kocher, M., Ruge, M. I., Galldiks, N., & Lohmann, P. (2020). Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahl. Onkol., 196, 856–867. https://doi.org/10.1007/s00066-020-01626-8
Kolker, E., Özdemir, V., & Kolker, E. (2016). How Healthcare Can Refocus on Its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson. Omics : a journal of integrative biology, 20(6), 329–333. https://doi.org/10.1089/omi.2016.0077
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of ambient intelligence and humanized computing, 14(7), 8459–8486. https://doi.org/10.1007/s12652-021-03612-z
Lee, D., & Yoon, S. N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International journal of environmental research and public health, 18(1), 271. https://doi.org/10.3390/ijerph18010271
Lee, M. B., Kramer, D. R., Peng, T., Barbaro, M. F., Liu, C. Y., Kellis, S., & Lee, B. (2019). Brain-Computer Interfaces in Quadriplegic Patients. Neurosurgery clinics of North America, 30(2), 275–281. https://doi.org/10.1016/j.nec.2018.12.009
Li, X., Zeng, L., Lu, X., Chen, K., Yu, M., Wang, B., & Zhao, M. (2023). A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges. Brain sciences, 13(7), 1056. https://doi.org/10.3390/brainsci13071056
Lin, D. J., Johnson, P. M., Knoll, F., & Lui, Y. W. (2021). Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians. Journal of magnetic resonance imaging : JMRI, 53(4), 1015–1028. https://doi.org/10.1002/jmri.27078
Liu, Z., Zhu, Y., Zhang, L., Jiang, W., Liu, Y., Tang, Q., Cai, X., Li, J., Wang, L., Tao, C., Yin, X., Li, X., Hou, S., Jiang, D., Liu, K., Zhou, X., Zhang, H., Liu, M., Fan, C., & Tian, Y. (2023). Structural and functional imaging of brains. Science China. Chemistry, 66(2), 324–366. https://doi.org/10.1007/s11426-022-1408-5
McBee, M. P., Awan, O. A., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018). Deep Learning in Radiology. Academic radiology, 25(11), 1472–1480. https://doi.org/10.1016/j.acra.2018.02.018
McCradden, M. D., Joshi, S., Mazwi, M., & Anderson, J. A. (2020). Ethical limitations of algorithmic fairness solutions in health care machine learning. The Lancet. Digital health, 2(5), e221–e223. https://doi.org/10.1016/S2589-7500(20)30065-0
Mier, W., & Mier, D. (2015). Advantages in functional imaging of the brain. Frontiers in human neuroscience, 9, 249. https://doi.org/10.3389/fnhum.2015.00249
Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. JAMA, 309(13), 1351–1352. https://doi.org/10.1001/jama.2013.393
Oettl, F. C., Oeding, J. F., Feldt, R., Ley, C., Hirschmann, M. T., Samuelsson, K., & ESSKA Artificial Intelligence Working Group (2024). The artificial intelligence advantage: Supercharging exploratory data analysis. Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 32(11), 3039–3042. https://doi.org/10.1002/ksa.12389
Olsen, S., Zhang, J., Liang, K. F., Lam, M., Riaz, U., & Kao, J. C. (2021). An artificial intelligence that increases simulated brain-computer interface performance. Journal of neural engineering, 18(4), 10.1088/1741-2552/abfaaa. https://doi.org/10.1088/1741-2552/abfaaa
Patel, U. K., Anwar, A., Saleem, S., Malik, P., Rasul, B., Patel, K., Yao, R., Seshadri, A., Yousufuddin, M., & Arumaithurai, K. (2021). Artificial intelligence as an emerging technology in the current care of neurological disorders. Journal of neurology, 268(5), 1623–1642. https://doi.org/10.1007/s00415-019-09518-3
Peksa, J., & Mamchur, D. (2023). State-of-the-Art on Brain-Computer Interface Technology. Sensors (Basel, Switzerland), 23(13), 6001. https://doi.org/10.3390/s23136001
Remsik, A., Young, B., Vermilyea, R., Kiekhoefer, L., Abrams, J., Evander Elmore, S., Schultz, P., Nair, V., Edwards, D., Williams, J., & Prabhakaran, V. (2016). A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke. Expert review of medical devices, 13(5), 445–454. https://doi.org/10.1080/17434440.2016.1174572
Savadjiev, P., Chong, J., Dohan, A., Vakalopoulou, M., Reinhold, C., Paragios, N., & Gallix, B. (2019). Demystification of AI-driven medical image interpretation: past, present and future. European radiology, 29(3), 1616–1624. https://doi.org/10.1007/s00330-018-5674-x
Segato, A., Marzullo, A., Calimeri, F., & De Momi, E. (2020). Artificial intelligence for brain diseases: A systematic review. APL bioengineering, 4(4), 041503. https://doi.org/10.1063/5.0011697
Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Mining and Knowledge Discovery, 13(2), e1485. https://doi.org/10.1002/widm.1485
Shih, J. J., Krusienski, D. J., & Wolpaw, J. R. (2012). Brain-computer interfaces in medicine. Mayo Clinic proceedings, 87(3), 268–279. https://doi.org/10.1016/j.mayocp.2011.12.008
Silva, G. A. (2018). A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence. Frontiers in neuroscience, 12, 843. https://doi.org/10.3389/fnins.2018.00843
Singh, K.R., Dash, S. (2023). Early Detection of Neurological Diseases Using Machine Learning and Deep Learning Techniques: A Review. Academic Press, 1-24. https://doi.org/10.1016/B978-0-323-90277-9.00001-8
Slutzky M. W. (2019). Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations. The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry, 25(2), 139–154. https://doi.org/10.1177/1073858418775355
Snoswell, C. L., Chelberg, G., Guzman, K. R. D., Haydon, H. H., Thomas, E. E., Caffery, L. J., & Smith, A. C. (2021). The clinical effectiveness of telehealth: A systematic review of meta-analyses from 2010 to 2019. Journal of Telemedicine and Telecare. https://doi.org/10.1177/1357633×211022907
Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023). Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. Sensors (Basel, Switzerland), 23(6), 3062. https://doi.org/10.3390/s23063062
Topol E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Tripathi, A.K., Ahmed, R. & Tiwari, A.K. (2024). Review of Deep Learning Techniques for Neurological Disorders Detection. Wireless Pers Commun, 137, 1277–1311. https://doi.org/10.1007/s11277-024-11464-x
Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689
Vrahatis, A. G., Skolariki, K., Krokidis, M. G., Lazaros, K., Exarchos, T. P., & Vlamos, P. (2023). Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors (Basel, Switzerland), 23(9), 4184. https://doi.org/10.3390/s23094184
Wang, A., Tian, X., Jiang, D., Yang, C., Xu, Q., Zhang, Y., Zhao, S., Zhang, X., Jing, J., Wei, N., Wu, Y., Lv, W., Yang, B., Zang, D., Wang, Y., Zhang, Y., Wang, Y., & Meng, X. (2024). Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial. Med (New York, N.Y.), 5(6), 559–569.e4. https://doi.org/10.1016/j.medj.2024.02.014
Wintermark, M., Colen, R., Whitlow, C. T., & Zaharchuk, G. (2018). The vast potential and bright future of neuroimaging. The British journal of radiology, 91(1087), 20170505. https://doi.org/10.1259/bjr.20170505
Zaharchuk, G., Gong, E., Wintermark, M., Rubin, D., & Langlotz, C. P. (2018). Deep Learning in Neuroradiology. AJNR. American journal of neuroradiology, 39(10), 1776–1784. https://doi.org/10.3174/ajnr.A5543
Zhang, X., Ma, Z., Zheng, H., Li, T., Chen, K., Wang, X., Liu, C., Xu, L., Wu, X., Lin, D., & Lin, H. (2020). The combination of brain-computer interfaces and artificial intelligence: applications and challenges. Annals of translational medicine, 8(11), 712. https://doi.org/10.21037/atm.2019.11.109
Zhu, G., Jiang, B., Tong, L., Xie, Y., Zaharchuk, G., & Wintermark, M. (2019). Applications of Deep Learning to Neuro-Imaging Techniques. Frontiers in neurology, 10, 869. https://doi.org/10.3389/fneur.2019.00869