Modernizing Asthma Diagnostics: Biosensors Enhanced by Nanomaterials and Artificial Intelligence

Asthma is a prevalent, long-term inflammatory airway condition that is difficult to diagnose and treat because there is no single reliable diagnostic test. Misdiagnosis is therefore common, with rates as high as 73% in juvenile groups and up to 35% in adult populations. This ultimately exacerbates their illness by postponing therapy for some people and administering needless medication to others. Although well-known biomarkers such as blood eosinophils and fractional exhaled nitric oxide, as well as conventional diagnostic techniques such as spirometry, have improved clinical assessment, they are nevertheless constrained in many healthcare settings by limited availability, high cost, and inconsistent use. Furthermore, these indicators primarily reflect type-2 inflammation and are less useful for non-type-2 asthma, highlighting the need for more comprehensive, readily accessible diagnostic techniques. Identifying novel biomarkers of oxidative stress, metabolic alterations, and airway inflammation, including volatile organic compounds and redox-related chemicals, has been the focus of recent studies. These biomarkers offer opportunities for improved disease phenotyping and non-invasive detection. Simultaneously, advances in biosensor technology have enabled highly sensitive platforms to rapidly detect these biomarkers at low concentrations. In particular, optical biosensors are becoming more and more popular due to their ability to do real-time detection without the need for labels and their ease of miniaturization for point-of-care devices. This work summarizes traditional diagnostic tools alongside existing information on asthma phenotypes and clinically important biomarkers, and discusses advanced biosensors ranging from electrochemical to optical systems, including recent developments in nanomaterial-enhanced optical biosensing techniques. The importance of artificial intelligence and smartphone-integrated hardware is also covered, along with the main challenges that need to be overcome for these technologies to become useful clinical tools for asthma diagnosis and monitoring.