2025 Highlights in digital technology in headache

Introduction

With the technological progress of our modern world, advances in digital technology follow that improve the diagnostics, treatment and understanding of headache disorders. In 2025, we have seen advances in the use of internet-based platforms, mobile applications, social media, wearables and artificial intelligence (AI).

The internet and social media have assumed an increasingly prominent role in the lives of individuals with headache, but nonetheless they hold a dual position facilitating both opportunities and challenges (1). These platforms promote community engagement and public awareness, increase access to information for individuals living with headaches, help disseminate educational resources, and serve as platforms for digital tools for self-management. On the contrary, the same platforms may spread misinformation and promote unqualified sources, fail to deliver individualized advice and guidance, exploit vulnerable groups and, paradoxically, potentially comprise headache triggers (1).

A 2025 review summarized the current landscape of the internet- and smartphone-based headache interventions (2). The internet and apps are primarily used to deliver behavioral change techniques and relaxation training, as well as coordinate management plans with patients and promote medication adherence. Among the published controlled trials of internet-delivered therapies, including two large-scale studies, none demonstrated clear superiority over other therapies or waitlist controls (2). Studies assessing app-delivered therapies have been optimistic, but mainly comprise pilot and feasibility trials, making it difficult to say anything conclusive about their efficacy (2).

Despite the optimism, a recalibration of our expectations came with a well-designed 2025 randomized controlled trial of a prescribable migraine treatment app (3). In this trial, 428 adults with migraine were randomized to the migraine app or a basic documentation app. The app included a headache-, trigger- and medication diary, a data report feature, and an active self-management feature including non-pharmacological interventions such as trigger management strategies. After 12 weeks of treatment, there was no significant difference between groups in the primary endpoint of monthly migraine days. This study underscores the need for rigorous evaluation before digital technology interventions may be established as effective and evidence based.

Other new and notable concepts are digital phenotyping and digital twin models. Digital phenotyping encompasses the real-time collection of environmental, lifestyle, mental and physiological data using smartphones, wearables and biosensors to provide insights to disease patterns, symptom fluctuations and treatment responses (4). In headache, digital phenotyping is predicted to have a number of benefits for both research and clinical practice (5). Continuous monitoring of objective data, both from patients and external sources, may reduce reliance on self-reported data and improve diagnostic accuracy. Similarly, analysis of multimodal continuously captured data may enable disease subtyping and characterization of treatment responses. All these strategies may naturally be enhanced by AI-driven pattern recognition and representational learning (5). An extension of digital phenotyping is the digital twin, broadly defined as a digital model of a physical process (5,6). In headache care, this would typically mean a virtual digital representation of a patient. This virtual representation could be created through automatically captured real-time information from technologies such as smartphones, wearables and biosensors. Possible applications of digital twins are in diagnostics, treatment selection, attack forecasting, patient counselling and telemedicine (6). As an example, the digital twin could identify phenotypic traits supporting specific headache diagnoses and red flags warranting investigations for secondary headaches. It could also temporally capture responses to both acute and prophylactic treatment, which in turn could help tailor treatments regimes to individuals.

Digital technology brings with it exciting advances in AI and machine learning. One area of growing interest is the prediction of treatment response. A 2025 systematic review evaluated such predictive models for migraine (7). Pooled analysis of six studies yielded an area under the curve performance of 0.86 (95% confidence interval = 0.67–0.95). Despite the impressive predictive performance, many of the studies had a high risk of bias and very small sample sizes, and none of the studies evaluated the models in a held-out test set, which limits generalizability. Such predictive models need to be evaluated in appropriate clinical settings, independent of the training data, before their utility may be properly assessed.

A noteworthy 2025 study proposed a method to leverage advanced computational models to better understand the genetic architecture of migraine (8). Using genome-wide genotype data from 43,197 individuals and machine learning, a model was developed that could significantly more often identify individuals with migraine compared to conventional polygenic risk scoring. This suggests that migraine may follow a non-additive and interactive genetic causal structure. Just as importantly, the study demonstrates the value of machine learning not only for predictive analyses, but also for increasing our understanding of headache disorders.

Another important publication calls for standardization of study designs in the application of AI to migraine classification (9). Particularly important is the implementation of rigorous and transparent reporting practices for machine learning model performance, including selection of appropriate scoring metrics and out-of-sample validation. Such standardized reporting should be mandatory to ensure transparency and interpretability, and in turn clinical applicability. This call for standardization extends beyond migraine diagnostics and classification and is applicable to all clinical tasks leveraged by AI, such as prognostication and treatment selection.

Finally, it is worth discussing how generative chatbots may be used in headache education and headache research, with benefits for both patients and clinicians (10). Chatbots may improve headache education through tailored, individualized information to patients, in turn increasing treatment adherence and reducing risk of chronification. Clinicians cloud benefit from easily accessible up-to-date information and clinical training simulations. In headache research, chatbots could help data collection and analysis, support clinical trials and facilitate experimental workflows. Still, these benefits must be carefully balanced against the potential pitfalls of over-reliance on AI (10).

To conclude, there are rapid developments in smartphone and wearable technologies, digital phenotyping, and AI. Although these technologies cause much excitement and are considered promising and potentially revolutionizing advances, their clinical utility still needs to be validated. The headache field should remain open to these innovations, but the promise of accessibility and scalability must not outpace the importance of empirical validation.