Symptom Checker

Digital symptom checker tools have become popular as preliminary healthcare, especially in developing countries where AI helps predict diseases early and diagnose doctors. But in Bangladesh, lack of professional healthcare access leads people to self-medication and misuse of prescription drugs, especially antibiotics. Lack of enforcement of drug dispensing rules worsens antibiotic misuse, thereby increasing the risk of antibiotic resistance, a global health concern. This scenario highlights the need for an accessible technological solution like the proposed AI-driven symptom checker, which can guide patients toward appropriate healthcare actions.
We have developed an AI-based symptoms checker to predict the recommended course of action, specifically whether patients should opt for over-the-counter (OTC) medications or seek a doctor’s consultation. The model combines multiple machine learning algorithms to maximize accuracy, using a curated dataset that includes patient demographics (age and gender) and medical conditions represented by 1,816 symptoms, symptom duration, and severity. The symptom checker project is focused on developing an application that guides patients in determining whether to use over-the-counter (OTC) medications or consult a doctor. To ensure the application provides accurate, relevant, and age-appropriate recommendations, a thorough evaluation process will be implemented. This will involve conducting multiple pilot studies with various pharmacies to assess real-world performance.