Artificial Intelligence in Clinical Neurology: Opportunities, Limitations, and the Path Forward

The integration of artificial intelligence (AI) into clinical medicine has rapidly advanced diagnostics, treatment planning, and workflow efficiency. In neuroimaging, AI can boost diagnostic accuracy, enable faster interpretation of complex scans, and help support decision-making in neurology and neurosurgery. Neurology depends on informed clinicians to guide the safe and effective adoption of AI. By understanding clinical AI and evaluating emerging tools, clinicians can harness its potential to personalize care and improve outcomes for people with neurologic disorders.

Despite its potential, integrating AI into neurology brings notable challenges, including concerns around data privacy, security, and the need for strong regulatory oversight. Continuous research is essential to refine algorithms and improve diagnostic accuracy and reliability. Ethical considerations and potential biases must also be addressed to ensure fair and responsible use in clinical care. This review examines current clinical applications of AI in neuroimaging, discusses its limitations and ethical implications, and outlines future directions for intelligent imaging systems.

Current Applications of AI in Clinical Neuroimaging

AI is reshaping clinical neuroimaging by improving diagnostic precision, streamlining workflows, and enabling more personalized care. Machine learning (ML), a core component of AI, allows systems to learn patterns from data without explicit programming. Within ML, deep learning (DL) uses multilayered neural networks to extract complex, hierarchical features from imaging data.¹ These technologies are being applied to tasks such as lesion detection, segmentation, disease classification, volumetrics, disease progression, and outcome prediction across various neurologic conditions.

Stroke Imaging and Triage

Radiology, and neuroradiology in particular, is well-positioned for AI integration due to the large volume of imaging data generated and physician shortages. Stroke, a time-critical and high-impact condition, has emerged as a leading application area for AI in neuroimaging. ML and DL techniques enable automated pattern recognition and predictive modeling, enhancing diagnostic accuracy and workflow efficiency. DL algorithms, especially those based on artificial neural networks, can detect large vessel occlusions on CT angiography with high sensitivity. AI-driven perfusion analysis quantifies ischemic core and penumbra, aiding thrombectomy decisions. Triage platforms such as Viz.ai (San Francisco, CA) and RapidAI (San Mateo, CA) can triage the stroke and integrate with hospital systems to deliver real-time alerts, reducing door-to-needle times.² AI enhances image quality and reconstruction by reducing scan time, improving spatial resolution, and minimizing noise and artifacts, thereby improving the speed and accuracy of stroke imaging.³ There is also a growing body of evidence related to hemorrhagic stroke, secondary prevention strategies, and stroke rehabilitation, highlighting the expanding opportunities and potential of AI in stroke neurology.²

Brain Tumor Characterization

Brain tumor characterization is essential for accurate diagnosis and prognosis, particularly in identifying genetic sequence variations associated with different tumor types. Radiomics and radiogenomics are emerging approaches that analyze disease features from imaging and genetic data, respectively. Radiomics extracts quantitative features from radiologic images; radiogenomics integrates these with genomic information to assess sequence variation status and guide personalized therapy. A notable application of radiogenomic modeling is the noninvasive prediction of IDH1 sequence variation status in gliomas, which can enhance diagnostic precision and inform personalized treatment planning.⁴

One of the most transformative applications of AI in neuro-oncology is automated segmentation, where convolutional neural networks precisely delineate tumor margins, surrounding edema, and necrotic tissue on MRI scans.⁵ This level of accuracy greatly enhances both surgical planning and radiotherapy targeting. During surgery, neurosurgeons benefit from real-time AI-assisted navigation tools that help navigate complex anatomy, identify key landmarks, and minimize tissue damage.⁶ These technologies are increasingly integrated into multidisciplinary tumor boards, enhancing collaborative decision-making and enabling more personalized, effective care for patients with brain tumors. The Figure presents an illustration of AI-empowered multidisciplinary brain tumor management.

Neurodegenerative Disorders

AI has a transformative role in the early detection and monitoring of neurodegenerative diseases and white matter disorders. ML algorithms can analyze imaging biomarkers, such as hippocampal volume, cortical thickness, and white matter characteristics with AI platforms such as NeuroDiscovery AI (Alpharetta, GA). Positron emission tomography isotopes for amyloid and tau can be used to detect early signs of dementia, often before clinical symptoms appear, supporting timely intervention and personalized care. Earlier detection of cerebral degeneration has allowed earlier and wider treatment with the increasing number of Food and Drug Administration (FDA)–approved therapies for Alzheimer disease (AD). These tools also enable automated quantification of amyloid and tau burden, supporting diagnosis and tracking disease progression. DL plays a vital role in addressing medical challenges by offering advanced tools for the early detection, diagnosis, and management of AD, and supports drug efficacy assessment by modeling drug–inhibitor interactions, offering a powerful approach to tailoring treatments.⁸

Beyond imaging, AI systems can generate detailed simulated biologic environments that evaluate the molecular and cellular mechanisms underlying conditions such as AD or stroke-related inflammation. Commercial systems, such as medical AI engines offered by Titans Forge (Clear Brook, VA),⁹ offer specialized AI agents to run rigorous biologic stress tests, putting virtual brains through high-pressure scenarios to reconstruct causal pathways and test therapeutic options under realistic conditions, from energy shortages to blood–brain barrier breakdown and inflammasome overdrive. By focusing on logic-first modeling before patient care application, this approach minimizes the enormous costs and high failure rates of traditional animal and human trials. This practical roadmap advances hypothesis generation and helps prioritize interventions with the strongest causal foundations, moving closer to strategies that can restore and protect neurologic function.⁹

Emerging AI-based neuroimaging tools are increasingly being used to standardize the detection and longitudinal monitoring of amyloid-related imaging abnormalities (ARIA). AI-assistive software, such as icobrain aria (Icometrix; Boston, MA), has demonstrated improved sensitivity and diagnostic accuracy for ARIA detection compared with unassisted radiologist interpretation while reducing interreader variability through automated lesion segmentation and quantification on MRI.¹⁰ In parallel, NeuroQuant 5.0 (Cortechs.ai; San Diego, CA) has received FDA 510(k) clearance for advanced automated MRI segmentation and lesion quantification.⁹ NeuroVision (NeuroDiscovery AI; Atlanta, GA), another tool that provides ARIA detection and monitoring, is currently available as a beta version, offered at no cost to all US neurologists and neurology groups.

Multiple Sclerosis and White Matter Disorders

In the era of precision medicine, AI is poised to transform the management of complex diseases such as multiple sclerosis (MS). By integrating large-scale patient data, including genomic information, biomarkers, imaging data, and real-world evidence, AI models can provide more precise and tailored treatment recommendations based on individual patient characteristics. AI algorithms can leverage longitudinal patient data and real-time monitoring to predict disease progression, relapse risk, and therapeutic response. In clinical neuroimaging, AI has proven effective in detecting and quantifying MS lesions on fluid-attenuated inversion recovery and T2-weighted MRI scans, even in subtle cases. Automated lesion segmentation improves diagnostic accuracy and supports longitudinal monitoring, enabling improved assessment of treatment response and relapse prediction.¹⁰ AI’s pattern recognition capabilities are also assisting in the identification of rare white matter disorders (eg, leukodystrophies), which often evade conventional radiologic interpretation.^11-13 Collectively, these advances enhance diagnostic confidence, support personalized disease management, and contribute to more timely and effective care. These tools improve reproducibility, reduce interreader variability, and enable sensitive longitudinal monitoring of disease activity and progression. Various companies are developing these technologies, including NeuroDiscovery AI (with NeuroVision), as well as Imeka (Boston, MA), BrainSpec (Boston, MA), Pixyl (La Tronche, France), and Neurophet (Seoul, South Korea).

Epilepsy

AI is offering new hope in the imaging of epilepsy, particularly in challenging MRI-negative cases. Advanced algorithms have improved the detection of focal cortical dysplasia and gray matter heterotopia, which can be subtle and are often missed by human readers.¹ In negative conventional scans, AI models trained on large data sets can identify imaging biomarkers that aid in diagnosis and treatment planning. Integration with electrophysiologic data and tractography enhances localization of epileptogenic zones, optimizing surgical strategies. These innovations contribute to better outcomes and reduced morbidity through more precise and personalized care.¹⁴

Workflow Efficiency and Quality Assurance

AI is enhancing workflow efficiency in neurology by automating routine tasks, streamlining clinical decision-making, and improving patient access and engagement. DL models are capable of detecting and correcting motion artifacts in MRI scans, improving image quality and reducing the need for repeat imaging, which is particularly useful for patients with neurologic disorders.¹⁵ AI-powered triage systems in neurology rapidly analyze imaging data to prioritize critical cases (eg, stroke, intracerebral hemorrhage), enabling faster interventions and improved outcomes. These tools also assist emergency departments by predicting admission and mortality risks using advanced ML models.² AI also enhances quality control by standardizing diagnostic workflows and reducing variability across clinical assessments, paving the way for more precise and equitable care. AI-enabled clinical scribes are increasingly being integrated into routine workflows to automate documentation and encounter summarization, thereby reducing clinician burnout, lowering cognitive workload, and decreasing documentation time, while enhancing patient access and clinician attention to patient concerns.¹⁶

Remote and cloud-based neuroimaging workflows are increasingly incorporating AI to automate image analysis, triage, and reporting, improving diagnostic efficiency while enabling real-time collaboration and timely prioritization of urgent findings across distributed care networks.¹⁷

Pitfalls and Limitations

AI in neuroimaging faces several challenges that must be addressed to ensure safe, equitable, and effective clinical deployment. One major concern is generalizability and data set bias, as AI models often struggle to perform consistently across institutions due to demographic underrepresentation and variations in scanner types, imaging protocols, and labeling practices.¹⁸ These limitations raise serious questions about equity and reliability in real-world settings.

The interpretability of AI systems—many of which function as opaque “black boxes” under the guise of protecting their proprietary algorithms—also must be considered. This lack of transparency in how decisions are derived can erode clinician trust and complicate informed consent. To address these concerns, explainable AI (XAI) is emerging as a vital advancement, providing clarity into the reasoning behind predictions and fostering greater confidence in AI-assisted care.¹⁹

Regulatory and legal hurdles also pose barriers, with many AI tools still awaiting FDA approval. Use of AI often does not have a clear liability framework. Data privacy and security are additional concerns. AI systems require access to large volumes of sensitive patient data, making Health Insurance Portability and Accountability Act compliance and protection against breaches essential, especially in cloud-based environments.²⁰ Furthermore, overreliance on AI may lead to automation bias and deskilling, where clinicians overly trust algorithmic outputs and trainees lose opportunities to develop crucial diagnostic skills.²¹ Economic and logistical barriers, such as the high financial and environmental cost of data center infrastructure and integration with existing systems, may hinder widespread adoption. These challenges risk widening disparities between institutions of well-resourced and underserved areas, limiting equitable access to AI-driven health care innovations. Addressing these multifaceted challenges is imperative for responsible and sustainable integration of AI into neuroimaging practice.

Future Directions

To address the limitations of AI in neuroimaging, several promising future directions are emerging that aim to enhance performance, equity, and clinical utility. One key advancement is multimodal integration, in which AI models combine data from imaging, clinical records, and genomic profiles to provide richer diagnostic insights and support personalized treatment planning, advancing the core principles of precision medicine.²² Data privacy and security are challenges in AI, especially in health care. Federated learning addresses these by enabling model training across institutions without sharing sensitive patient data. This decentralized approach enhances generalizability while safeguarding privacy. Collaborative networks further support model validation across diverse clinical settings, ensuring reliability without compromising data security.²³ XAI plays a vital role in making AI systems more transparent and trustworthy in clinical practice. By highlighting specific imaging features that influence predictions, XAI helps clinicians understand how and why a model arrives at its conclusions. This interpretability is crucial for building confidence in AI-assisted decisions.¹⁹

AI systems must also evolve through continuous learning and real-world validation to remain reliable in clinical practice. This involves incorporating feedback loops from users and adapting to new, diverse data beyond controlled trial environments. Such dynamic updating ensures models stay relevant and accurate as clinical conditions and patient populations change.²² Interdisciplinary collaboration is essential, with AI tools designed to support joint decision-making across radiology and neurology, thereby promoting holistic and patient-centered care. The development of ethical and humanistic guardrails is essential, including bias auditing, patient-centered design principles, and clear accountability frameworks to ensure that AI technologies serve humanity responsibly and equitably.²⁴

The future of neuroimaging is not defined by a single algorithm, but by intelligent systems that translate complex imaging data into clinically meaningful insight. As AI matures, its greatest impact will be in reducing cognitive and operational burden, automating quantitative tasks such as volumetry, lesion detection, and longitudinal tracking, and enhancing standardization, reproducibility, and scalability across scanners and institutions. Rather than replacing clinician expertise, modern AI functions as a clinical decision support layer, enabling neurologists and radiologists to focus on synthesis, context, and patient-centered judgment. The integration of multimodal data—imaging, clinical history, cognitive metrics, and eventually genomics—marks a shift from image interpretation toward precision neurology, where disease phenotyping and progression can be measured rather than inferred.

Looking ahead, vision-language and modular expert-based AI systems represent evolution in how clinicians interact with neuroimaging. By combining visual understanding with natural language reasoning, these systems allow clinicians to ask clinically intuitive questions of imaging data and receive transparent, interpretable outputs supported by visual maps and quantitative metrics. This architecture—built on task-specific expert models rather than monolithic “black boxes”—supports explainability, adaptability, and trust, which are essential for real-world clinical adoption. As these tools become embedded within workflows, the neurologist’s role will increasingly shift from image reader to integrator of AI-augmented insights, guiding diagnosis, monitoring disease trajectory, and personalizing care while remaining firmly in the decision loop.

Neurologists can stay up-to-date with the use of AI in neuroimaging by combining regular engagement with the medical literature, active institutional involvement, and professional collaboration. Beyond reading, using AI tools within one’s own institution, such as algorithms for stroke detection, tumor segmentation, or longitudinal disease monitoring, provides practical insight into real-world performance and limitations. Neurologists should also seek involvement in hospital or health system AI governance, digital health, or imaging informatics committees, where they can help guide algorithm selection, validation, ethical oversight, and clinical integration. Participation in professional meetings and AI-focused sessions, as well as collaboration with radiologists, data scientists, and informatics teams, further ensures ongoing exposure to evolving technologies and best practices in AI-driven neuroimaging.

Conclusion

AI is revolutionizing clinical neuroimaging by enhancing how neurologic conditions are diagnosed, prioritized, and managed. AI has shown promise in improving care for stroke, brain tumors, dementia, MS, and epilepsy, leading to better outcomes and more efficient workflows. However, important challenges remain. Issues such as bias in algorithms, lack of interpretability, regulatory hurdles, and high costs must be addressed to ensure that AI is used safely and fairly across diverse patient populations.

AI is not intended to replace clinicians, but to be thoughtfully integrated as a tool that supports and enhances human expertise. Achieving this will require collaboration across disciplines, a strong commitment to ethical standards, and ongoing innovation. The potential is exciting; cautious optimism is essential. As we embrace AI, we must stay grounded in the principles of transparency, equity, and patient-centered care to ensure it truly benefits those it is meant to serve.