[Development and validation of Cefalytics: an artificial intelligence-based application for the rapid diagnosis of primary headaches].

OBJECTIVE: To design and validate a Spanish-language artificial intelligence-based application to support the diagnosis of primary headaches by non-expert physicians. DESIGN: Retrospective observational study for diagnostic development and validation based on clinical records. SETTING: Neurology outpatient clinics receiving patients referred from primary care (level 2 headache care). PARTICIPANTS: A total of 1,029 adult patients with a diagnosis of 17 types of primary headache confirmed by an expert neurologist according to the criteria of the International Classification of Headache Disorders, 3rd edition (ICHD-3), treated at two hospital centers between 2022 and 2024 were included. MAIN OUTCOME MEASURES: Sensitivity, specificity, F1-score and accuracy of the rule-based diagnostic algorithm and the supervised learning model. RESULTS: The application integrates a structured clinical questionnaire that includes most primary headache diagnoses defined in the ICHD-3. The rule-based algorithm achieved a sensitivity of 89.8% (95% CI: 87.9-91.5), specificity of 99.4% (95% CI: 99.2-99.5), F1-score of 91.8% (95% CI: 90.3-93.1), and accuracy of 93.3% (95% CI: 91.5-94.8). The supervised learning model showed a sensitivity of 88.1% (95% CI: 84.0-91.8), specificity of 99.1% (95% CI: 98.6-99.5), F1-score of 88.5% (95% CI: 84.6-92.0), and accuracy of 85.5% (95% CI: 79.3-91.4). CONCLUSIONS: Cefalytics (https://cefalytics.com/) shows high diagnostic performance and may represent a useful support tool for non-expert physicians, particularly in primary care, facilitating greater diagnostic accuracy and earlier therapeutic optimization. These results require external validation in an independent cohort within this setting.