AI-DRIVEN PERSONAL HEALTHCARE: AN INTEGRATED SYSTEM FOR SYMPTOM-BASED OTC MEDICATION SELECTION AND LOCAL AVAILABILITY TRACKING

Abstract

This article presents the development and evaluation of an innovative artificial intelligence (AI) system designed to enhance personal healthcare management through intelligent over-the-counter (OTC) medication selection and local availability tracking. The system employs natural language processing (NLP) to analyze user-described symptoms, recommending appropriate OTC medications while considering potential drug interactions and contraindications. It integrates with local pharmacy inventories to provide real-time availability and pricing information. A comprehensive evaluation of the system demonstrates its effectiveness in accurately recommending medications (accuracy rate: 92.7%), efficiently locating nearby pharmacies with available stock (average search time: 3.2 seconds), and significantly improving user satisfaction in OTC medication selection (user satisfaction score: 4.6/5). The system successfully addresses common challenges in self-medication, including inappropriate drug selection and difficulty in locating specific medications. Combining symptom analysis, medication recommendation, and local availability tracking, this AI-driven approach offers a valuable tool for empowering consumers to make informed healthcare decisions. The findings highlight the potential of AI in revolutionizing personal healthcare management and pave the way for future research in integrating AI with pharmaceutical retail networks for enhanced public health outcomes.