Machine learning capabilities in headache diagnosis - Jaworowska, Julia; Osiński, Damian; Kawa, Zuzanna; Kasprzak, Maria; Jędrzejewska, Aleksandra; Jureczko, Aleksandra; Kleczaj, Klaudia; Levadna, Valentyna; Babiarz, Gabriela; Kanarszczuk, Julia (Quality in Sport, 2026)
Introduction: Headaches can be divided into primary and secondary types. Primary headaches include migraine, tension-type headache, and cluster headache - trigeminal- autonomic cephalalgia. Secondary headaches are symptoms of other illnesses, often life- threatening, such as cerebrovascular diseases. In diagnosis, artificial intelligence (AI) is increasingly helpful to physicians, as it can independently recognize and even classify headaches using machine learning. Research objective: Review of scientific literature and summary of current machine learning capabilities in headache diagnosis Materials and Methods: A literature review was conducted using PubMed and Google Scholar databases. The following keywords were used: "Machine learning headache", "Machine learning headache diagnosis", "Artificial intelligence and headache". Selected articles are in the time frame of 2020-2025. Results: Machine learning (ML) is most often used in classifying headaches using a patient’s medical records or imaging tests with an accuracy of up to 90%. Based on functional magnetic resonance imaging (fMRI), it is possible to identify primary headaches and classify them. Additionally, thanks to AI, people who do not specialize in headaches can more easily diagnose and treat patients suffering from this disease. AI also enables predicting the occurrence of a headache and distinguishing between primary and secondary headaches. Conclusions: The development of machine learning in the field of headache diagnosis requires additional research, gathering more data, and verifying it in clinical practice.