Artificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review

Abstract

Background

Headache disorders, particularly migraine, are highly prevalent, but often remain underdiagnosed and undertreated. Artificial intelligence (AI) offers promising applications in diagnosis, prediction of attacks, analysis of neuroimaging and neurophysiology data, and treatment selection. Its use in headache medicine raises ethical, regulatory, and clinical questions, including its impact on the doctor–patient relationship.

Methods

A systematic literature search was conducted on April 10, 2025, across PubMed, Cochrane Library, Scopus, Web of Science, and DOAJ, following PRISMA guidelines. Two reviewers independently applied strict inclusion criteria to select studies published from 2000 to 2025 in either English or Spanish. Risk of bias was assessed using validated tools tailored to study design, including the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), Prediction Model Risk of Bias Assessment Tool (PROBAST), Newcastle–Ottawa Scale (NOS), and Appraisal Tool for Cross-Sectional Studies (AXIS).

Results

A total of 76 studies were included in the qualitative synthesis. The analysis covered AI methodologies, clinical applications, patient perspectives, and ethical implications. AI tools have shown potential to improve diagnostic accuracy, headache subtype classification, and prediction of treatment response, and may help reduce the administrative burden in clinical practice. Emerging technologies such as digital twins, wearable biomarker monitoring, and synthetic data generation support personalized approaches and may reshape clinical research. However, significant challenges remain. These include data quality, model interpretability, algorithmic bias, privacy concerns, and regulatory gaps. Moreover, the evidence base is still developing, with expectations often exceeding the strength of available clinical data. Many studies present methodological limitations due to small sample sizes, selection bias, and lack of external validation, which limit their generalizability to real-world settings. Finally, concerns about depersonalization and transparency affect patient trust in AI, reinforcing the need for both human oversight and a patient-centered approach.

Conclusions

AI holds promise for improving headache care, but evidence supporting its clinical utility is still limited. Integration into practice must be rigorously validated, ethically guided, and carefully designed to prevent depersonalization. Human oversight remains essential as AI should complement, not replace, clinical judgment.

Introduction

Headache disorders remain among the most prevalent, disabling and costly neurological conditions [1–3]. The diagnosis of primary headache disorders continues to pose significant clinical challenges due to overlapping symptomatology, subjective symptom reporting, and the lack of objective biomarkers [4–6]. This reliance on clinical diagnostic criteria can lead to misdiagnosis and delay the initiation of appropriate treatment, particularly when patients are evaluated by non-headache specialists [7, 8]. Furthermore, effective management is complicated by the challenge of selecting the most suitable treatment for each patient, as therapeutic responses can vary widely. Close clinical monitoring is often required to adjust therapy over time and optimize outcomes. However, implementing this level of care in routine practice is often difficult due to the high prevalence of headache disorders and the resulting burden on clinical resources [9].

In recent years, artificial intelligence (AI) has emerged as a transformative tool in medicine, capable of analyzing complex datasets, identifying subtle diagnostic patterns, and supporting personalized therapeutic decisions [10]. In headache medicine, AI holds particular promise for improving diagnostic accuracy, refining subtype classification, identifying potential triggers, predicting headache attacks, anticipating treatment response, and enabling real-time symptom monitoring through digital technologies [11–13]. Moreover, AI facilitates research by extracting structured clinical data from electronic health records, identifying key clinical variables, and enabling advanced data analysis [12]. However, this rapid shift toward automation also raises important ethical challenges and may impact the doctor-patient relationship, particularly in a field where trust, empathy, and nuanced communication are essential [14, 15].

This review explores the evolving role of AI in the diagnosis, treatment, and research, while critically addressing its ethical implications and its potential impact on the clinician–patient relationship.

Methods

Study identification

The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [16]. A comprehensive literature search was performed on April 10, 2025, across the following electronic databases: PubMed, Cochrane Library, Scopus, Web of Science, and the Directory of Open Access Journals (DOAJ). The search process was independently carried out by two reviewers (CEV and MCM). The following search strings were used:

#1 “Artificial Intelligence“[mh] OR. “Machine Learning“[tw] OR “Deep Learning“[tw] OR “Natural Language Processing“[tw] OR “Algorithm“[tw].

#2 “Headache Disorders“[mh] OR migraine*[tw] OR “tension-type headache“[tw] OR “cluster headache“[tw] OR “headache“[tw].

#3 #1 AND #2.

#4 “Diagnosis, Computer-Assisted“[mh] OR “Clinical Decision Support Systems“[mh] OR “Neuroimaging“[tw] OR “Biomarkers“[tw] OR “Predictive Value of Tests“[mh].

#5 “Wearable Electronic Devices“[mh] OR “Mobile Applications“[mh] OR “Digital Health“[tw] OR “Telemedicine“[mh] OR “Remote Consultation“[tw].

#6 “Physician-Patient Relations“[mh] OR “Medical Ethics“[mh] OR “Trust“[tw] OR “Privacy“[mh] OR “Algorithmic Bias“[tw] OR “Humanism“[tw].

#7 #3 AND (#4 OR #5 OR #6).

#8 #7 AND (“2000“[dp]: “2025“[dp]) AND (English[la] OR Spanish[la])

Study selection

The authors, CEV and MCM, independently assessed all articles based on their titles and abstracts. Articles that appeared to meet the eligibility criteria outlined in Table 1, which pertain to the use of AI in headache medicine, were selected for further review.

Table 1.

Eligibility criteria for articles

Inclusion criteria	• Human studies • Peer-reviewed full-text articles only • Published between January 2000 and March 2025 • Research addressing the use or application of AI in headache medicine (including diagnosis, management, prediction, digital tools, or doctor-patient interaction) • Articles written in English or Spanish
Exclusion criteria	• Animal studies or in vitro experiments • Articles not available in full text • Systematic reviews, editorials, commentaries, or letters without original data • Studies not directly addressing AI in the context of headache disorders

Discrepancies between the two reviewers were settled by discussion, until a mutual agreement was reached. The final lists of studies were then reviewed to identify any additional eligible articles not captured in the initial search.

Data synthesis

Studies were categorized according to their methodological design and the presence of standardized performance metrics (e.g., accuracy, sensitivity, specificity, or AUC). Only studies with measurable outcomes were included in the structured synthesis and quality assessment. Narrative or conceptual articles were excluded from the quantitative synthesis but acknowledged in the broader context. This approach aimed to preserve methodological rigor and ensure comparability.

Risk of bias and methodological quality assessment

Risk of bias was assessed independently by two reviewers (CEV and MCM) using validated tools tailored to each study design. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was applied for diagnostic accuracy studies and implemented using RevMan (Cochrane Collaboration). Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for prediction model studies, the Newcastle–Ottawa Scale (NOS) for cohort and case-control studies, and Appraisal Tool for Cross-Sectional Studies (AXIS) for cross-sectional studies. Only studies with adequate methodological detail and standardized quantitative outcomes were appraised. Disagreements were resolved by discussion.

Results

Study selection and overview

A total of 76 studies were included in the qualitative synthesis (Fig. 1). Publication frequency increased substantially after 2013, peaking in 2023 (Fig. 2A). Migraine was the most commonly studied headache subtype, followed by tension-type and cluster headaches (Fig. 2B). The included studies covered diverse AI methodologies, clinical applications, patient outcomes, and ethical considerations.

Fig. 1.

Article selection process

Fig. 2.

Overview of selected articles on AI in headache disorders. (A) Timeline showing the publication years of papers focused on AI in headache. (B) Distribution of the number of papers categorized by headache type

Risk of bias within included studies

Of the 76 studies included in the review, 28 met the criteria for formal risk of bias assessment. The reported performance metrics varied, with accuracy ranging from 70 to 98%, and AUC values between 0.75 and 0.95 in externally validated models. Complete study characteristics and outcomes are detailed in the Supplementary Material (Table 2). Ten studies were evaluated using QUADAS-2 (Table 3). Most showed low risk in the index test domain, though several presented moderate concerns in patient selection and reference standards, with unclear flow and timing in about half of the studies. Fourteen studies were assessed using PROBAST (Table 4); although participant selection and outcome measurement were generally of low risk, many models lacked external validation, limiting their applicability and reliability. Three cohort or case-control studies were appraised using the NOS (Table 5), revealing moderate methodological quality, mainly due to limitations in sample representativeness and adjustment for confounders. One cross-sectional study was assessed with AXIS (Table 6); despite addressing a relevant clinical question with real-world data, it lacked a control group, external validation, and ethics approval.

Table 2.

Summary of included original studies reporting AI performance metrics in headache disorders

AI Type	Number of Studies	Headache Type	Accuracy (%)	AUC	Reference
SML	1	Mig, TTH	Approx. 85–92	NR	[46]
SML	1	Mig	Balanced accuracy > 84	NR	[65]
SML (SVM)	1	Primary headache	Sensitivity: 89%, Specificity: 73%	NR	[43]
SML (SVM, MLP) + Fuzzy Expert System	1	Mig	88–91	NR	[39]
Unsupervised	1	TTH	NA	NA	[47]
Symbolic	1	Multiple types (ICHD-3 based)	NR	NR	[40]
HUL	1	Mig, TTH, Others	75.0	NR	[48]
SML	6	Mig	75–98	0.73–0.95	[49]
DL + Unsupervised (K-means)	1	EM	NR (regression)	NR	[45]
SML (SVM)	1	Mig, CH	78–99	NR	[70.]
SML	1	Mig, MOH, TTH	76.25 (phase 1); 46–83.2 (phase 2)	NR	[41]
SML	1	Mig (80%)	90.78	NR	[62]
SML	1	Mig	NR (R² = 0.89)	NR	[52]
SML	1	Primary (Mig, TTH, CH) vs. Secondary	~ 74 (balanced accuracy)	NR	[53]
SML	1	HFEM	85.71	90.91	[83]
SML	1	CH	88.4	0.87	[86]
SML	1	Mig	NR	0.62	[58]
DL	1	Primary Headache (Mig, TTH, CH)	88.3	NR	[73]
DL	1	Mig	NR	0.71	[68.]
SML	1	Mig	80.6	NR	[36]
SML	3	Mig, CM, TTH, TAC, Fibromyalgia	86.8–99.66	0.9–0.96	[26.]
SML	1	Mig (7 subtypes)	88.2–99.66	NR	[24]
SML	1	Mig, TTH	81.3	NR	[10]
NLP + SML (SVM, LR, NB)	1	Mig	82–86 (F1-score)	NR	[27]
SML	1	Mig, MOH, TTH	94.92	0.95	[29]
SML	1	Pediatric and Adolescent Mig	94.5	NR	[44]
DL	1	Mig (EM and CM)	58.1–80	0.581–0.825	[84]
SML	1	CM	≥ 70	0.87–0.98	[82]
SML	1	TTH	77.38–95	NR	[77]

AI, artificial intelligence; CH, cluster headache; CM, chronic migraine; DL, deep learning; EM, episodic migraine; HUL, hybrid unsupervised learning; LR, logistic regression; MLP, multi-layer perceptron; Mig, migraine; NA, not applicable; NLP, natural language processing; NR, not reported; PPV, positive predictive value; QUADAS-2, Quality Assessment of Diagnostic Accuracy Studies-2; SML, supervised machine learning; SVM, support vector machine; TAC, trigeminal autonomic cephalalgia; TTH, tension-type headache

Table 3.

QUADAS-2 risk of bias and applicability assessment of included studies

Study Reference	Patient Selection (RoB/Applic.)	Index Test (RoB/Applic.)	Ref. Standard (RoB/Applic.)	Flow & Timing (RoB/Applic.)	Key Performance Metrics
[43]	High (retrospective EMR)	Low (SVM model)	Low	Moderate	Moderate diagnostic performance
[51]	Moderate (unclear criteria)	Low (algorithm described)	Moderate (ref. standard unclear)	Moderate	Moderate diagnostic performance
[49]	Moderate (retrospective, unclear sampling)	Low	Moderate	Low	Moderate diagnostic performance
[70]	Low	Moderate (index test unclear)	Moderate	Moderate	Moderate diagnostic performance
[41]	Low	Low (AI model reported)	Low	Low	Moderate performance; external validation performed
[45]	Low to Moderate (retrospective)	Low to Moderate (limited index test detail)	Low to Moderate	Low to Moderate	Accuracy 90.78%; sensitivity 90.78%; other metrics not fully reported
[29]	Moderate	Moderate (ML approach, limited transparency)	Low	Low	Accuracy 94.92%; κ = 0.65; migraine sensitivity 98.21%, specificity 66.67%
[10]	Low (prospective registry, large sample)	Low (XGBoost, LASSO)	Low (ICHD-3 diagnoses)	Low	Accuracy 81%; migraine sensitivity 88%, specificity 95%; lower sensitivity for other headache types
[44]	Low (retrospective pediatric, large sample)	Low (PyCaret, cross-validation)	Low (ICHD-3 diagnoses by neurologists; no external validation)	Low	Accuracy 94.5%; sensitivity 88.7%, specificity 96.5%, precision 90%, F1-score 89.4%
[42]	Low (adult prospective clinics/community)	Low (CDE, online self-assessment)	Low (semi-structured interview)	Low	κ = 0.83 (almost perfect agreement); sensitivity 90.1%, specificity 95.8%; PPV 97%, NPV 86.6% (based on clinical prevalence)

AI, artificial intelligence; CDE, computer-based diagnostic engine; EMR, electronic medical records; F1-score, harmonic mean of precision and recall; ICHD-3, International Classification of Headache Disorders, 3rd edition; κ, kappa statistic; LASSO, least absolute shrinkage and selection operator; ML, machine learning; NA, not applicable; NLP, natural language processing; NPV, negative predictive value; PPV, positive predictive value; PyCaret, Python-based machine learning tool; RoB, risk of bias; Applic., applicability; SVM, support vector machine

Table 4.

PROBAST risk of bias and applicability assessment of included studies

Study Reference	Sample Size	Headache type(s)	RoB Participants	RoB Predictors	RoB Outcome	RoB Analysis	Comments
[46]	1022	Mig, TTH	Low	Low	Unclear	High	10-fold CV, AUC (NR); possible overfitting
[65]	7	Mig	High	Unclear	Unclear	High	Very small sample, high risk for bias
[39]	190	Mig, TTH, CH	Low	Low	Unclear	High	90/10 split, AUC (NR)
[88]	1446	CM	Low	Low	Low	High	Low AUC (0.54–0.69), poor discrimination
[62]	4375	EM + NM	Low	Low	Low	High	R² reported, AUC (NR), holdout + CV
[83]	145	CM + EM	Low	Low	Low	Low	External validation, AUC up to 0.91
[86]	254	CH	Low	Low	Low	Low	External validation, AUC ~ 0.85
[73]	5356	Mig, TTH, CH	Low	Low	Unclear	High	Internal test only, AUC (NR)
[68]	61,826	Mig	Low	Low	Low	Low	Large dataset, 60/40 split, AUC 0.71
[82]	712	EM + CM	Low	Low	Low	Low	80/20 split, AUC 0.87–0.98
[53]	121,241	Primary vs. Secondary	Low	Low	Low	Low	Large dataset, multi-validation, accuracy ~ 74%, ROC reported
[52]	40	Mig with Aura	High	Unclear	Low	High	Small sample, wrapper method, AUC (NR)
[77]	64	TTH	High	Low	Low	High	Small sample, AUC (NR)
[71]	4260	EM + CM	Low	Low	Low	Moderate	AUC ranges 0.58–0.83, moderate risk due to performance variability

AUC, area under the curve; CH, cluster headache; CM, chronic migraine; CV, cross-validation; EM, episodic migraine; Mig, migraine; NM, new daily persistent headache; NR, not reported; R², coefficient of determination; ROC, receiver operating characteristic; RoB, risk of bias; TTH, tension-type headache

Table 5.

NOS risk of bias and applicability assessment of included studies

Study Reference	Selection (max 5)	Comparability (max 2)	Outcome (max 3)	Total Score (max 10)
[27]	5	1	3	8/10
[47]	4	–	2	6/10
[36]	1	–	3	4/10

Table 6.

AXIS risk of bias and applicability assessment of included studies

Study Reference	Quality Tool	Main Strengths	Main Limitations
[87]	AXIS	Clear objectives, real-world data, expert system, no conflicts of interest	No control group, no external validation, no ethical approval reported

This structured evaluation highlights the methodological heterogeneity among the included studies and underscores the need for more rigorous and externally validated AI applications in headache research.

Discussion

Fundamentals of AI in headache

Despite its name, AI relies on mathematical and statistical models capable of processing large datasets and improving through training and iterative use [17]. Although some systems are inspired by biological processes, many aim to replicate aspects of neural function to classify data and generate predictions [18]. AI is generally categorized as weak AI, focused on specific tasks, or strong AI, which aspires to general autonomous intelligence [19, 20]. The emergence of large language models (LLMs) suggests a transition toward strong AI, while artificial superintelligence, capable of recursive improvement and value alignment, is now the subject of active research and ethical debate [20]. For headache specialists, AI literacy is essential to navigate this evolving field and engage in its clinical application, ensuring alignment with patient needs, ethical principles, and the humanization of care [21, 22].

Machine learning, deep learning, and natural Language processing

Machine learning (ML) underpins artificial intelligence by allowing systems to learn from data without explicit programming [23]. In supervised learning, models are trained using labeled data to perform tasks such as classifying migraine versus non-migraine cases [18, 23, 24]. In contrast, unsupervised learning uses unlabeled data to detect intrinsic groupings without predefined categories (for example, differentiating among migraine, cluster headache, and tension-type headache). Meanwhile, reinforcement learning, although still uncommon in this field, has theoretical potential for modeling dynamic decision-making processes, such as optimizing treatment plans over time based on evolving symptoms [25].

Deep learning (DL), a subset of ML, employs multilayered neural networks to integrate heterogeneous data sources, including clinical records, omics, imaging, and video, detecting subtle patterns across thousands of MRI or MEG scans [18]. DL has been used to distinguish chronic migraine from fibromyalgia and healthy controls based on MEG connectivity patterns [26].

Natural language processing (NLP) enables the extraction of clinically relevant data (e.g., attack frequency, treatment response) from unstructured text [27] and supports the development of medical chatbots for guided clinical interviews.

Transformers and generative AI

Transformers are a type of neural network that revolutionized how computers process language, thanks to a technique called self-attention. This allows them to understand not just word order but also meaning and context [28]. This technology powers LLMs like GPT-4, which can understand and generate text, images, voice, and video with almost human-like skill. Although still emerging in headache medicine, transformer-based models have been applied to classify headache types using diagnostic frameworks with promising accuracy [29]. Their ability to interpret complex multimodal data suggests potential for broader decision-support applications [30].

Generative AI refers to AI systems, often built on deep learning, that can create new content from scratch. This includes writing text, making images, producing audio or videos, and more [31]. In headache research, it could accelerate drug discovery, generate anonymized patient data to train models, and simulate clinical trials [32]. However, generative AI has a key risk: it can produce false but convincing information, known as hallucinations. For this reason, expert review and ethical oversight are essential [33].

Emerging role of AI in headache

Innovations in AI have the potential to transform headache research and clinical practice. Four promising areas include: digital twins for personalized simulation, wearable-based monitoring for real-time symptom tracking, therapeutic virtual reality, and AI-driven clinical research models that improve study design and data analysis [22, 34].

At the patient level, a digital twin is an emerging concept that aims to create a virtual replica integrating medical history, multi-omics, pharmacokinetics, neuroimaging, and real-time wearable data [22, 32]. It serves as a personalized sandbox for simulating interventions and predicting outcomes without risk to the patient. For instance, it may identify effective preventive therapies or the likelihood of migraine chronification. The concept also supports virtual clinical trials, disease modeling, full clinic simulations, and even the simulation of an entire headache clinic [32, 34]. Additionally, it may transform clinical research through synthetic data generation and in silico trial simulations, potentially improving study design and reducing risks to real patients [35].

Continuous monitoring through wearable devices is becoming increasingly sophisticated, though its clinical utility still requires validation [36]. Future devices may include transdermal sensors for biomarkers like CGRP, EEG headbands or diadems to monitor brain activity, and smart contact lenses to track autonomic activity via pupillary changes [22].

Virtual reality (VR) and augmented reality (AR) are emerging as potential tools in headache medicine [11]. VR can simulate controlled environments to study symptoms like photophobia, aura, or vestibular disturbances. Combined with biofeedback, VR may help modulate pain by adapting sensory input in real time through AI. Therapeutically, VR combined with biofeedback may help modulate pain by adapting sensory input in real time through AI [11].

AI and machine learning in headache diagnosis and classification

AI and ML have emerged as powerful tools to augment both diagnosis and classification by analyzing large and complex datasets [37–39]. In diagnosis, AI-driven systems such as the Computer-based Diagnostic Engine (CDE) have demonstrated high reliability, achieving over 90% accuracy, with excellent sensitivity and specificity in identifying both primary and secondary headaches [40]. These tools excel particularly in fast-paced environments like emergency departments, where timely and accurate differentiation of headache types is crucial [29, 41]. Moreover, AI models have shown robust performance across different age groups, accurately diagnosing migraine in both adults and children using gradient boosting and random forest algorithms [42, 43].

Building upon diagnostic success, AI also enhances the classification of headache subtypes by leveraging structured patient data, such as standardized questionnaires. While classification involves systematically grouping headaches based on established criteria like the ICHD 3, diagnosis focuses on identifying the specific subtype in individual patients through detailed symptom evaluation. ML models have effectively distinguished between common headache subtypes, including migraine, tension-type headache (TTH), and cluster headache, with accuracies often exceeding 90% [10, 13, 29, 44, 45]. Advanced hybrid approaches, combining statistical clustering and ML methods like fuzzy c-means, discriminant analysis, neural networks, and hybrid K-means/Random Forest algorithms, have further refined classification precision, achieving accuracies as high as 99% [46–49].

These models can even differentiate nuanced subtypes, such as migraine with aura, from healthy controls, demonstrating their potential for fine-grained diagnostic support [50].

The utility of IA extends beyond classification and diagnosis into clinical triage and risk stratification. For example, random forest models trained on extensive patient datasets can distinguish primary from secondary headaches with high specificity, assisting clinicians in prioritizing urgent cases and alleviating healthcare system burdens [29, 51].

Despite these promising developments, challenges remain before widespread clinical implementation. Many AI models require further external validation, and their performance may decline when applied to rare headache subtypes or more diverse patient populations [10, 52]. Continued efforts to develop more generalizable models trained on large and heterogeneous datasets will be essential to fully realize the potential of AI in headache medicine.

Together, classification provides the essential framework of headache subtypes, while diagnosis is the practical application of this framework to individual patients. This process is increasingly supported and refined by AI tools capable of integrating complex clinical data, thereby improving diagnostic accuracy and enhancing patient care [11].

AI-driven prediction of migraine attacks, medication overuse, and care needs

Migraine attack prediction is a promising application of artificial intelligence, as early treatment improves therapeutic efficacy and helps reduce the anxiety associated with the unpredictability of attacks [53–55]. Accurate prediction depends primarily on identifying individual triggers, recognizing premonitory symptoms, and detecting physiological changes that precede migraine onset [56, 57]. Since these variables are complex, diverse, and highly individualized, AI can help identify migraine-related triggers and physiological changes that patients may not consciously perceive, enabling both personalized attack forecasting and early treatment. An increasing number of patients are using tools like Migraine Buddy and Migraine Manager not only as headache diaries, but also to track triggers and symptom patterns, enhancing self-care and supporting clinical decisions [58, 59].

A recent study applied deep learning algorithms to over 330,000 headache episodes recorded via a smartphone diary, demonstrated that changes in barometric pressure, humidity, and rainfall can predict migraine occurrence with significant accuracy [60]. Considering the relevance of weather changes in migraine, AI-driven diaries and apps are becoming valuable tools to improve diagnostic accuracy by integrating contextual data such as meteorological variables [61, 62].

AI-driven analysis of wearable sensor data can identify physiological patterns that precede migraine attacks, enabling early detection with promising accuracy. In a pilot study with a limited sample (n = 7), physiological changes during sleep (including heart rate, skin conductance, and temperature) were measured using a wrist-worn Empatica E4 sensor, achieving over 84% balanced accuracy in predicting migraine attacks one night in advance [63]. Another approach focused on detecting autonomic nervous system changes during pre-ictal sleep using wearable biosensors. The analysis of electrodermal activity and skin temperature in 10 individuals emerged as a key predictor of migraine attacks, with an accuracy of 0.806 [36]. Finally, Stubberud et al. combined app-based headache diaries with self-administered physiological measurements in 18 patients to train machine learning models, with random forest classifiers achieving an AUC of 0.62 for predicting migraine attacks [56].

AI can support clinical decision-making during follow-up by helping identify patients who may benefit from closer monitoring or treatment adjustments [64, 65]. It also helps uncover behavioral traits and clinical features linked to care-seeking and disease burden, as demonstrated in the OVERCOME study, where machine learning models were applied to data from 61,826 individuals with migraine. Of them, 51% sought medical care in the previous 12 months, and seeking care was strongly associated with severe interictal burden (OR 2.64), functional disability (OR 2.2), and ictal cutaneous allodynia (OR 1.7) [66]. These findings highlight the potential of AI to support early identification of patients more likely to require clinical attention.

Additionally, the identification of medication overuse headache (MOH) offers another example of how AI can help flag individuals in need of timely intervention. In a study involving 777 migraine patients, a machine learning–based decision support system (RO-MO) was developed to predict MOH using clinical, biochemical, and lifestyle variables. The model showed strong performance, with a c-statistic of 0.83, and stratified patients according to their risk of MOH, yielding odds ratios of 5.7 and 21.0 for probable and definite overuse, respectively [67].

AI applications in headache imaging and neurophysiology

Among the most promising applications of AI in headache research are neuroimaging and neurophysiology, where ML and DL algorithms assist in processing large, complex datasets to identify diagnostic biomarkers, classifying headache subtypes, and predicting prognosis [34, 68, 69]. In neuroimaging, AI has been applied to analyze structural MRI, rs-fMRI, and PET data to detect subtle brain alterations linked to headache disorders. Deep learning models have demonstrated high performance in distinguishing migraine with and without aura patients from healthy controls, using resting-state fMRI data and functional metrics such as regional functional correlation strength, achieving accuracies of up to 99.25% [70].

AI has also been used to differentiate primary from secondary headaches, as shown in a study classifying MRI scans from patients with migraine and post-traumatic headache, which achieved up to 91.7% accuracy and identified distinct brain regions relevant to each type [71]. In addition, deep learning techniques applied to BOLD-fMRI and cortical brain mapping have shown potential to distinguish between primary headache subtypes, such as migraine and tension-type headache, thereby contributing to a better understanding of their underlying pathophysiology [72]. Together, these findings highlight the role of AI in improving diagnostic accuracy and advancing the characterization of primary headache disorders.

AI is increasingly extending its utility to neurophysiological testing, showing potential to differentiate ictal from preictal phases in migraine and to support the diagnostic classification of headache syndromes. For instance, EEG complexity analysis can predict the preictal phase of migraine up to 72 h before onset with 76% accuracy [73]. Similarly, Martins et al. used a wearable EEG system for daily at-home monitoring in patients with episodic migraine. The authors identified significant neurophysiological changes, specifically altered delta and beta power and reduced P300 amplitude, occurring 24 h before attack onset. These findings suggest that portable EEG may support early detection of the preictal phase [74].

In a different application, ML models analyzing low-frequency brain activity have also achieved 95% accuracy in distinguishing TTH headache from other primary headache disorders by identifying abnormalities in regions such as the cerebellum and hippocampus [75]. Moreover, magnetoencephalography (MEG) combined with AI algorithms has proven effective in differentiating chronic migraine from other neurological conditions by detecting subtle changes in cortical network dynamics [26].

AI in headache therapeutics

AI may offer potential support in guiding personalized treatment strategies for headache disorders, encompassing both non-pharmacological and pharmacological approaches. Non-pharmacological interventions guided by AI, including digital cognitive behavioral therapy and personalized dietary modifications, may help reduce migraine frequency and improve quality of life [76, 77]. A pilot study found that a biofeedback–virtual reality device reduced analgesic use and depression in chronic migraine, supporting its potential as a nonpharmacological adjunct therapy [78].

In parallel, pharmacological strategies are being refined through AI-driven integration of clinical, demographic, genetic, and imaging data to tailor treatments to individual patient profiles [11, 66, 79, 80]. ML algorithms can accurately predict therapeutic responses to pharmacological options like NSAIDs, botulinum toxin, and anti-CGRP therapies [80–82]. Tools like the AI-driven Relivion^® system personalize nerve stimulation by analyzing patient data in real time, while predictive models identify key response factors such as the age of onset and comorbid anxiety [83].

AI may contribute to treatment optimization by continuously analyzing clinical and wearable data to detect pharmacological treatment failure and support timely therapeutic adjustments [56, 84, 85]. It facilitates earlier intervention and informs drug selection, shortening the time to efficacy by over 35% in chronic migraine [86]. Additionally, AI aids in target discovery, with ML revealing molecular pathways like glutamine metabolism and cAMP regulation that may guide future migraine therapies [87]. These capabilities position AI as a cornerstone of precision, efficiency, and innovation in modern headache care.

Building trust in AI for diagnosis and treatment

AI is increasingly influencing the doctor–patient relationship. Survey data indicate that while patients generally view AI positively for its potential to enhance healthcare, they also express concerns regarding diagnostic errors, privacy violations, reduced physician interaction, and increased healthcare costs [15]. Moreover, there is a consistent preference for human advice over automated recommendations [88].

Trust plays a critical role in the adoption of AI in clinical settings [89]. However, studies reveal that many patients lack confidence in the ability of the healthcare system to use AI responsibly and continue to favor human clinical evaluation [90]. Factors such as user-friendliness, accuracy, and the perception that AI supports rather than replaces clinical judgment are central to its acceptance. Therefore, AI tools intended for diagnosis or treatment must comply with medical device regulations before clinical use [12]. To this end, many systems undergo a silent trial phase, during which algorithms run in parallel without influencing clinical decisions [91]. Notably, retrospective analyses have shown that AI classifiers can outperform general practitioners in diagnosing primary headache disorders [92]. Consequently, AI should be perceived as a clinical aid that enhances physician capabilities and optimizes workflow, rather than as a disruptive or autonomous entity [93].

Another key factor in building trust is ensuring that patients understand how AI-based decisions are made. However, the technical complexity of these systems often presents a barrier to comprehension not only for patients but also for clinicians [94]. A major limitation is the so-called black box problem, in which DL models fail to offer transparent explanations for their outputs. In healthcare, where human variability and nonlinear processes are inherent, this lack of interpretability can undermine trust. Addressing these challenges requires a more sophisticated systems-thinking approach to AI development, aimed at improving clarity and accessibility for both neurologists and patients [95].

The role of the neurologist in validating artificial intelligence

Importantly, AI should not replace healthcare professionals or the relationships they cultivate with their patients [89]. Rather, its primary function should be to assist clinicians and enhance diagnostic accuracy [47]. Neurologist involvement is essential for validating AI-generated results and fostering patient confidence. This includes clearly communicating the diagnostic process, addressing concerns, and providing support with empathy [96]. Indeed, for many patients, how a diagnosis is conveyed can be just as important as the diagnosis itself.

Despite its benefits, AI remains susceptible to misinterpretation and diagnostic errors, underscoring the need for human accountability [97]. Patients generally expect physicians to be responsible when errors occur in AI-assisted care [15]. Nevertheless, legal precedents in this area are limited and evolving [98]. As current legislation typically absolves physicians of liability when adhering to the standard of care, the safest strategy is to use AI as a confirmatory tool that supports, rather than replaces existing clinical decision-making processes [99].

Ethics and safety in AI implementation

AI raises significant ethical and safety concerns that require robust clinical validation, algorithmic transparency, protection of sensitive data, and mitigation of bias to prevent harm or inequality [100, 101]. In migraine care, its predictive potential is promising, but responsible implementation is essential to avoid misinformation and promote equitable access [13]. These concerns are more prevalent among minority groups, highlighting the need for ethical, transparent, and patient-centered implementation [15].

The use of mobile applications for migraine introduces additional risks. Although many include accessible privacy policies, they often allow the collection and use of personal data for commercial purposes without providing sufficient safeguards for users, especially when operating outside regulatory frameworks such as HIPAA or COPPA [102].

In pain medicine, the subjectivity of symptoms adds complexity to the use of AI. Therefore, the use of explainable models, specific regulation, and the inclusion of AI ethics specialists has been proposed to ensure respect for human dignity [103].

Regulatory considerations in AI integration

As AI tools increasingly enter clinical practice, robust and transparent regulatory frameworks are essential to ensure their safety and effectiveness. In Europe, the European Medicines Agency (EMA), together with the European Commission, has developed oversight mechanisms such as the EU Artificial Intelligence Act and the EMA Reflection Paper on the use of AI in the medicinal product lifecycle [104]. These classify many healthcare-related AI/ML systems as high-risk, requiring conformity assessments, post-market monitoring, and risk mitigation plans.

The EMA emphasizes that AI applications influencing therapeutic decisions must undergo rigorous review, with clear guidance for clinicians and safeguards in case of system failure. Similarly, the European Health Data Space (EHDS) and GDPR stress data protection and transparency. To promote trustworthy AI, the EMA supports alignment with the Assessment List for Trustworthy Artificial Intelligence (ALTAI), and early engagement with regulators is encouraged [105]. The European Commission has also approved guidelines under Regulation (EU) 2024/1689 to clarify obligations for general-purpose AI models [104].

In parallel, the U.S. Food and Drug Administration (FDA) regulates AI-based tools as Software as a Medical Device (SaMD), promoting Good Machine Learning Practice (GMLP), real-world performance monitoring, and predefined control plans for adaptive algorithms. Approval pathways vary depending on risk, and ongoing oversight is crucial for post-deployment updates [106, 107].

Importantly, the effective integration of AI in headache care requires neurologists to play an active role in evaluating the interpretability, clinical utility, and safety of these tools. This clinical engagement helps ensure that AI supports, rather than replaces, decision-making. Both the EMA and FDA underline human oversight, transparency, and risk mitigation as essential pillars for responsible AI use in healthcare [104, 106].

Risk of depersonalization in neurological care

Modern medicine faces the challenge of preserving human connection in increasingly complex healthcare systems. AI can assist doctors with diagnosis and treatment, and automate repetitive tasks like generating medical reports, reviewing headache diaries, and requesting tests [10, 11, 37, 38, 85]. This reduces the administrative burden, lowers the risk of medical burnout, and helps create more human-centered care [34].

In headache medicine, AI can analyze patient-reported data and adjust follow-up scheduling based on patient evolution, an approach that also supports more personalized and human-centered care. By taking over these time-consuming duties and helping to prioritize care based on clinical needs, AI allows physicians to focus their attention where it is most valuable: observing the patient rather than the computer, listening attentively rather than typing, and engaging in a clinical conversation rather than reviewing or completing forms.

It might be tempting to think that AI can replace physicians by diagnosing and treating patients based solely on symptom input. Direct patient interaction with AI offers immediacy, anonymity, and a space to ask questions without fear of being judged. However, its use raises relevant questions from the perspective of humanistic medicine: Can AI-only consultations truly enhance the experience of the patient in medical settings? Does a person feel heard when speaking to a machine? We all agree that caring for someone with headache requires more than following diagnostic criteria or therapeutic guidelines. It involves interpreting nonverbal cues, addressing fears, and building a relationship of trust. Empathy, emotional intelligence, and the ability to offer comfort are core aspects of medical practice, qualities that AI currently cannot replicate [108, 109].

Taking a medical history is more than collecting information: it means capturing what the patient does not say, what they need to express to someone who understands them. Moreover, AI cannot adapt its message to the patient emotional context or perceive subtle clinical signs that, when interpreted sensitively, can lead to the correct diagnosis or modify the therapeutic approach. The treatment of headache requires a patient-centered approach that considers the whole person, including their feelings, experiences, and personal context.

In the case of patients with chronic or refractory headache, a group that represents a high percentage of those attending specialized headache units, it is especially important to ensure strong physician–patient interaction because clinical guidelines often fall short of addressing their complex and multifaceted needs. These patients frequently present with multiple comorbidities, particularly psychiatric conditions, that require more than protocol-based decision-making. They need a physician who can interpret emotional cues, adapt communication to the psychological reality of the patient, and build a relationship of trust over time, dimensions that AI, by its nature, cannot access. Another key limitation is that AI cannot perform a physical examination. In patients with headache, especially during first consultations, a detailed neurological assessment is mandatory.

The implementation of AI in medicine is inevitable. But if adopted passively or without a humanistic framework, it carries a real risk of depersonalizing clinical care. There is a danger that physicians may gradually withdraw from the therapeutic relationship, limiting themselves to validating the outputs of AI systems, while losing the motivation to reflect, to study, and to connect meaningfully with their patients. This disengagement undermines the very essence of medicine. Therefore, physicians and AI should complement each other. Maximizing the potential of AI potential in headache care requires specific training of neurologists in digital tools and data analytics [97].

Technology can contribute greatly to headache management, but the art of medicine still lies in the human encounter, in the touch, in the words, and in the gaze that bring meaning and comfort to suffering [92, 96]. When integrated responsibly, AI can give us back something essential for our patients: time to care, to listen, to interpret, and to accompany. Used with purpose, AI can help restore the human dimension of medicine, in line with the description by Pellegrino of medicine as ‘the most humane of sciences, the most empiric of arts, and the most scientific of humanities” [110].

Limitations

The current evidence supporting AI in the field of headache disorders remains limited; integrating AI into healthcare systems, particularly for headache management, should be guided by four fundamental ethical principles: beneficence, non-maleficence, autonomy, and justice [111]. More studies are needed to rigorously evaluate the integration of AI within this ethical framework, especially to ensure that these principles are upheld in clinical practice.

Most published studies on AI and headaches face significant methodological limitations, including small sample sizes, selection bias, and lack of external validation, which restrict their generalizability to real-world settings [92, 96, 112]. This variability also leads to heterogeneity among studies, which precluded the use of a single risk of bias tool suitable for all study designs. To address this, we applied established quality appraisal instruments tailored to each study design, including QUADAS-2, NOS, PROBAST, and AXIS. Only studies with sufficient methodological detail and quantitative data were formally assessed, while those lacking standardized outcomes or adequate reporting were excluded from formal appraisal but included in a narrative synthesis to preserve relevant insights. This approach ensured a rigorous and tailored evaluation of study quality while acknowledging the diversity of methodologies in AI headache research.

Furthermore, the lack of transparency and interpretability in many AI systems (black box problem) raises understandable concerns among clinicians [95]. Additional unresolved challenges include the security and privacy of personal health data. Robust safeguards are essential to protect sensitive patient information, minimize algorithmic biases, such as poorer model performance in underrepresented groups, which could perpetuate health inequities, and establish regulatory frameworks that ensure the safety and efficacy of AI tools before their widespread clinical deployment [103].

Collectively, the results highlight the pressing need for studies with larger and better controlled datasets, as well as rigorous external validation, to improve the reliability, reproducibility, and clinical applicability of artificial intelligence models in the diagnosis and management of primary headache disorders.

Conclusions

AI offers significant potential in advancing headache diagnosis and care, but its implementation brings important ethical and safety challenges. Key concerns include data privacy, algorithmic bias, and the risk of reducing human interaction in clinical settings. Building trust in AI requires transparent, explainable systems, equitable access, and strong regulatory oversight that prioritizes patient autonomy and preserves the integrity of the doctor–patient relationship.

The future of AI in headache medicine lies in its integration into clinical practice, enhancing diagnosis, treatment personalization, and patient monitoring through tools like hybrid AI-clinician models and wearable technology. To move from innovation to implementation, robust validation, interpretability, and clinician training are essential. Maintaining human oversight will be crucial to ensure AI complements rather than replaces the doctor–patient relationship.

Abbreviations

AI

Artificial Intelligence

ALTAI

Assessment List for Trustworthy Artificial Intelligence

AR

Augmented Reality

AXIS

Appraisal tool for Cross-Sectional Studies

BOLD-fMRI

Blood-Oxygen-Level Dependent Functional Magnetic Resonance Imaging

cAMP

Cyclic Adenosine Monophosphate

CDE

Computer-based Diagnostic Engine

CGRP

Calcitonin Gene-Related Peptide

COPPA

Children’s Online Privacy Protection Act

CV

Cross-Validation

DL

Deep Learning

DOAJ

Directory of Open Access Journals

DP

Date of Publication

EEG

Electroencephalogram

EHDS

European Health Data Space

EMA

European Medicines Agency

FDA

Food and Drug Administration

GDPR

General Data Protection Regulation

GMLP

Good Machine Learning Practice

HIPAA

Health Insurance Portability and Accountability Act

ICHD-3

International Classification of Headache Disorders 3rd Edition

LLMs

Large Language Models

MDATES

Migraine Diagnostic Automation Expert System

MEG

Magnetoencephalography

ML

Machine Learning

MOH

Medication Overuse Headache

MRI

Magnetic Resonance Imaging

NLP

Natural Language Processing

NOS

Newcastle–Ottawa Scale

NSAIDs

Nonsteroidal Anti-Inflammatory Drugs

OR

Odds Ratio

PET

Positron Emission Tomography

PMA

Premarket Approval

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PROBAST

Prediction model Risk Of Bias ASsessment Tool

QUADAS-2

Quality Assessment of Diagnostic Accuracy Studies 2

RevMan

Review Manager (Cochrane)

rs-fMRI

Resting-State Functional Magnetic Resonance Imaging

SaMD

Software as a Medical Device

TPLC

Total Product Lifecycle

TTH

Tension-Type Headache

VR

Virtual Reality

Author contributions

CEV and MCM screened and selected the articles obtained from the database search. CEV MCM, DE, AAV, MGR and PI prepared figures and tables. CEV, MCM, MCM, AAV, and MGR wrote the original draft of the review. PI and DE supervised the review writing. All authors read and approved the final manuscript.CEV* and MCM*. These authors contributed equally to this work, and share first authorship.

Funding

This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The data supporting the findings of this study, including the protocol and data extraction sheets, are openly available in the Open Science Framework (OSF) at: https://osf.io/bxkjw/?view_only=72e3ebec22f8434785969952d4fb43eb.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests. Dr. Pablo Irimia has received personal fees for Speaker/travel grants from Teva, Allergan/Abbvie, Eli Lilly, Organon, Lundbeck, Novartis, Dr. Reddy’s and Pfizer.