Large language models have demonstrated substantial progress in mastering the foundational knowledge base of neurology, as reflected in their performance on standardized board-style examinations. GPT-4 correctly answered 82% of U.S. neurology board-style questions (n = 1,956), surpassing GPT-3.5 (66%) and approaching the lower range of specialist performance [6]. Three large language models (Bard, Claude 2, and GPT-3.5) were evaluated on 20 essay-style advanced neurophysiology questions, which were scored by physiologists on a 0–5 scale, yielding a mean score of 3.9/5 across models [7]. On the Spanish Neurology Specialist Examination (77 multiple-choice questions), GPT-4 scored 81.8% correct—ranking seventeenth among 120 neurologists who took the examination [8]. On 200 multiple-choice questions comparable to the neuroscience section of the United States Medical Licensing Examination (USMLE), Claude (88.0%) and GPT-4 (81.7%) outperformed the student average (74.6%) [9]. On the NeuroReady® board preparation question bank (n = 400), GPT-4 scored 75%, exceeding the passing threshold (70%) and the average test-taker score (69%) [10]. Collectively, these findings suggest that advanced large language models such as GPT-4 have reached near-human competence in factual neurology knowledge [6–12].

Accurate lesion localization based on signs and symptoms remains a defining cognitive skill of neurologists [13–15]. In a structured evaluation of 46 acute stroke vignettes derived from published cases, GPT-4 localized brain lesions with an F1 score of 0.85 for brain region and 0.74 for lesion side. Although no direct human comparison was performed, performance varied by region, with best results for cerebral and spinal lesions and poorest for cerebellar lesions [16]. Most localization errors arose from incomplete input data or reasoning gaps rather than factual hallucination, supporting GPT-4’s potential for structured neuroanatomical reasoning.

The diagnostic accuracy of large language models in neurology varies by setting and model. Current evidence suggests that while general-purpose large language models lag behind human neurologists in real-world practice, domain-specialized models can approach or exceed human performance in selected scenarios.

In a comparative study using 28 real-world anonymized patients, neurologists achieved substantially higher diagnostic accuracy (75%) than general-purpose large language models such as GPT-3.5 (54%) and Gemini (46%) [17]. These models struggled with complex clinical reasoning, contextual integration, and subtle diagnostic differentiation.

Similarly, OpenBioLLM—a domain-specialized model—achieved only 38% diagnostic accuracy on 25 cases from the textbook Clinical Cases in Neurology. Although the model frequently identified relevant symptoms and correctly localized the lesion, it often failed to arrive at an accurate etiologic diagnosis [18].

In contrast, the neurology-specialized model Neura demonstrated substantially higher diagnostic capability [19]. In a blinded comparison with 13 neurologists using five difficult Clinical Reasoning cases from the journal Neurology, Neura achieved scores of 86% overall, 85% for differential diagnosis, and 88% for final diagnosis. Neurologists scored 55%, 46%, and 71%, respectively. Neura produced rapid, citation-backed responses with minimal hallucination.

General-purpose large language models have also shown strong performance in specific tasks such as seizure localization. When presented with 1,269 clinical epilepsy narratives, Mistral-8x7B (F1 = 51.7) and GPT-4 (F1 = 52.3) localized epileptogenic zones across seven brain regions as accurately as expert neurologists (F1 = 48.8) [20].

Acute stroke management is time-critical and data-intensive, making it an ideal domain for integration with large language models. Modern stroke care produces large volumes of unstructured text—from triage notes, radiology findings, and procedural reports—that must be interpreted rapidly for treatment decisions [25]. Large language models optimized for stroke care can integrate clinical narratives with non-contrast CT findings to diagnose stroke, assess eligibility for intravenous thrombolysis, and detect large vessel occlusions. Song et al. [26] fine-tuned ChatGLM-6B based on 1,885 patients with and without stroke. Patients were divided into training and validation sets. The model distinguished between hemorrhage and infarction with 100% accuracy, identified large vessel occlusions with 80% accuracy, and screened patients for intravenous thrombolysis with 89.4% accuracy. The model input was a non-contrast CT scan and the clinical notes [26]. These findings highlight the potential of large language models to streamline time-sensitive stroke workflows, speed diagnosis, and enhance decision support.

Large language models are increasingly applied to the diagnosis and management of neurodegenerative disorders, particularly Alzheimer’s disease. They show promise for early detection by identifying subtle, complex symptom patterns within unstructured clinical text that may escape human recognition. Harrison et al. [27] provide a comprehensive review of emerging applications of large language models in improving Alzheimer’s disease diagnosis. Qadri et al. [28] review the utility of large language models in the diagnosis of neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. In an innovative approach, Zamai et al. [29] used fine-tuned large language models to classify 615 MRI images into four diagnostic categories (normal, Alzheimer’s disease, frontotemporal dementia, and primary progressive aphasia). The authors first converted each MRI into a synthetic radiology-style text report (an image-to-text approach) and then fine-tuned a language model to interpret these reports. With fine-tuning, Qwen-3.1-8B achieved a balanced accuracy of 68.4%, outperforming GPT-4o (balanced accuracy 55.5%) on the same classification task [29].

The diagnosis of Parkinson’s disease relies heavily on clinical observation of motor symptoms, which can lead to delayed or uncertain diagnosis in early stages. Advanced AI frameworks that integrate deep learning with natural language processing have been developed to analyze voice, gait, and motor patterns, enabling detection of subtle features of parkinsonism that may escape routine clinical examination. Twala [30] evaluated a multimodal model that combined gait analysis, voice analysis, and visual motor assessments to classify 847 synthetic patient profiles as having Parkinson’s disease or not. Using this synthetic dataset, the system achieved a diagnostic accuracy of 94.2%. Although these results are preliminary and limited by reliance on synthetic data, they highlight the potential of multimodal AI systems to enhance early detection of Parkinson’s disease.

Large language models such as GPT-4 are being evaluated for classifying multiple sclerosis (MS) status based on clinical notes. When aligned with diagnostic frameworks such as the 2017 McDonald criteria, they achieve classification accuracies of up to 74% [31]. Venkatesh et al. [31] used GPT-4 to reclassify 125 patients (105 with MS, 10 with related disorders, and 10 healthy controls) based on their clinical notes that included laboratory findings. GPT-4 correctly classified 74% (93/125) patients, including 70% of patients with MS, 100% of related disorders, and 90% of healthy controls. Large language model–based systems are increasingly applied to prognosis, integrating MRI data and large clinical registries to identify key predictors of disease progression and treatment response in multiple sclerosis [32].

In epilepsy, large language models have been evaluated for challenging diagnostic distinctions, such as differentiating epileptic seizures from functional or dissociative seizures. In a study using patient-generated symptom descriptions, Ford et al. [33] tested GPT-4 on 41 cases (16 epilepsy, 25 functional/dissociative seizures). In the zero-shot condition, GPT-4 achieved a balanced accuracy of 57% (κ=0.15), which improved to 64% (κ=0.27) after a single example (one-shot prompting). Additional examples (two- and three-shot) did not further improve performance. In contrast, three experienced neurologists achieved a mean balanced accuracy of 71% (κ=0.42). Notably, in the subset of 18 cases correctly diagnosed by all three neurologists, GPT-4 achieved a balanced accuracy of 81% (κ=0.66), suggesting that performance improves substantially when clinical descriptions are clear and internally consistent. Large language models have also been explored for presurgical planning. Fine-tuned systems can localize seizure origins to epileptogenic zones using clinical narratives, streamlining early stages of the preoperative workflow [34]. In addition, language models are increasingly used to assist with generating structured reports for electroencephalograms (EEG), electromyograms (EMG), and nerve conduction studies [25].

Taken together, these disease-specific applications should be viewed as exploratory rather than definitive. Most studies used small or moderate sample sizes, were conducted at single centers, or relied on synthetic data, vignettes, or retrospectively assembled datasets. External validation was limited, and few evaluations tested performance in real-time clinical workflows. As a result, the reported accuracies are best interpreted as suggestive of what large language models may be able to do under controlled conditions, rather than as evidence that they are ready for routine clinical deployment.

Bias arises from imbalanced training data. The overrepresentation of specific populations, institutions, or languages produce systematic inequities in diagnosis, classification, and treatment recommendations [25, 35–37].

Large language models risk re-identifying protected health information through memorization or inadvertent data exposure. Breaches, leaks, and technical vulnerabilities must be managed through robust encryption, audit trails, and data-minimization protocols [38].

Trust depends on reliability, validity, and explainability. Reliability ensures consistent results; validity aligns with clinical ground truth; and explainability enables oversight and clinician confidence [39–43].

Neurological reasoning depends heavily on subtle bedside findings that must be correctly observed and documented by the clinician [13, 44]. Large language models can only reason over what is recorded. This reflects the classical Garbage In, Garbage Out (GIGO) principle: incomplete or imprecise clinical inputs propagate into incomplete or erroneous model outputs. No current AI system can substitute for the neurologist’s direct examination; the fidelity of LLM- or LMM-generated reasoning is ultimately constrained by the quality of the clinician’s initial observations.

Some large language models may qualify as medical devices, requiring validation, traceability, and postmarket surveillance. Current regulatory frameworks remain incomplete, demanding coordinated oversight among developers, clinicians, and regulators [45–47].

Neurological diagnosis requires synthesizing text, imaging, and physiologic signals. While contemporary large language models (LLMs) are primarily trained on text and therefore depend on narrative descriptions of MRI, EEG, and EMG findings, emerging large multimodal models (LMMs) can natively integrate information from multiple data streams [48]. These multimodal architectures show promise in harmonizing radiologic, electrophysiologic, and textual inputs, potentially reducing the need for intermediate text-based summaries in the future [49–53]. Although the terminology is still evolving, and the boundary between LLMs and LMMs is not yet fixed, neurology is likely to benefit disproportionately from models that can directly process high-dimensional clinical signals.

Retraining and manual updates are slow and prone to catastrophic forgetting. Retrieval-augmented generation (RAG) architectures provide a more scalable solution, coupling dynamic knowledge sources with stable reasoning engines [54, 55].

Foundation models offer scalability and multimodal reasoning, but domain-specific models achieve higher accuracy and lower hallucination rates for clinical tasks [19].

Large language models perform well on structured examinations, yet show variable accuracy in real clinical contexts. Real-world validation remains the decisive test of their readiness for clinical use [56].

Neurology large language models should target reduction of documentation burden, automation of text summarization, and seamless integration with the EHR. EHR burden remains one of the greatest challenges facing neurologists [57, 58]. The design of large language models for neurologists must reflect clinician priorities—not replace—neurologist expertise [59, 60].

Large language models may function as active cognitive partners—suggesting differential diagnoses, proposing tests, or challenging initial hypotheses—while neurologists retain executive authority. The clinician’s role could shift subtly from synthesizing raw data toward validating and contextualizing AI recommendations, particularly in ambiguous or ethically complex cases [5, 25]. It is possible that advances in language-model–driven documentation will reduce the long-standing burdens associated with electronic health records, although the extent of such improvement will depend on technical reliability and clinical integration.

Integration of digital twins and multi-omic data may enable more personalized disease trajectory forecasting and treatment selection [61–64]. Future multimodal frameworks could cross-validate predictions to reduce bias and achieve greater fluency across text, imaging, and physiological signal data [8]. AI-generated digital twins [65–67] might one day support in silico comparisons of therapeutic strategies—analogous to virtual “clinical trials of one”—for disorders such as multiple sclerosis or Parkinson’s disease. Whether such simulations will become routine clinical tools remains uncertain and will depend on regulatory oversight, validation, and clinician acceptance.

A persistent barrier to real-world deployment of neurology-focused AI is the transformation of free-text clinical documentation into structured, computable, and interoperable data. Neurology-specific models may eventually achieve deeper fluency with biomedical ontologies—including SNOMED CT, HPO, GO, RxNorm, ICD-CM and with FHIR resource standards, thereby improving the consistency and computability of clinical data. Continuous alignment with curated databases and knowledge graphs may further reduce the need for frequent retraining, although such capabilities remain aspirational at present [68, 69]. Real progress will require: (1) consistent use of standardized vocabularies to encode diagnoses, medications, laboratory values, and neurological findings; (2) broad adoption of FHIR resources and profiles to represent encounters, observations, imaging studies, and procedures; and (3) robust NLP and mapping pipelines that convert narrative notes into coded concepts without losing clinically relevant nuance. Our preliminary work suggests that large language models can preprocess physician-written notes to improve downstream extraction of standard ontology terms and facilitate FHIR resource generation [70, 71]. Given that 60%–80% of clinically relevant information in U.S. electronic health records remains buried in free text [72, 73], expanding computability could meaningfully enhance both clinical care and research. However, this transition will require secure data platforms, governance frameworks, and auditable interfaces that allow AI systems to query and write back to the EHR safely. Without such infrastructure, even highly capable neurology models will remain confined to pilot settings rather than routine clinical practice.