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Background Diagnosis of asthma remains challenging due to its heterogeneity, symptom variability, and the absence of a universally accepted gold standard. Clinical diagnosis often depends on weighing evidence for asthma against evidence of plausible alternative conditions, guided by the principle of inference to the best explanation. 1 Aim To develop and evaluate a structured, multi-modal diagnostic template that systematically incorporates key clinical features supporting a diagnosis of asthma versus alternative diagnoses.Methods Within the SMART study ( NCT05357274), participants with a prior clinical diagnosis of asthma and ongoing symptoms despite inhaled corticosteroid (ICS) therapy were assessed using a diagnostic template (figure 1a and 1b).2 This multi-dimensional approach synthesised elements from existing clinical guidelines and trial criteria, including markers of allergy, Type 2 inflammation, lung function, treatment response, and structured exclusion of alternative conditions. Diagnosis was not based solely on clinician judgment but on systematic template-based assessment.Results Application of the multi-modal template confirmed asthma in 63 of 113 participants (56%), highlighting significant diagnostic uncertainty within the previously diagnosed population, outlined in figure 1c.Abstract S50 Figure 1Conclusion A structured, template-based approach improves diagnostic rigour by replicating and standardising expert clinical judgment, integrating all relevant domains of evidence. This method enhances diagnostic consistency compared with guideline-based clinical assessment alone. However, it is time-consuming and resource-intensive, underlining the need for future development of more efficient, scalable diagnostic tools.References Lipton P, Inference to the Best Explanation, 2nd edition, Routledge, 2003. https://doi.org.10.4324/9780203470855A System to Classify Treatable Traits in Primary Care (SMART). ClinicalTrials.gov: NCT05357274. Posted 2022–05-02.