AN OVERVIEW ON: MEDICAL SYMPTOMS CHECKER APP

Abstract: The rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has facilitated the development of automated medical symptom checkers, designed to bridge the gap between initial patient concern and professional clinical diagnosis. This research evaluates the efficacy, diagnostic accuracy, and user-centric design of digital triage platforms that utilize probabilistic modeling and natural language processing (NLP) to interpret patient- reported symptoms. By analyzing large datasets of clinical encounters, these systems aim to provide actionable health insights while mitigating the burden on primary care facilities. However, challenges persist regarding the clinical safety of automated advice and the potential for "cyberchondria" among users. This paper presents a comparative analysis of leading symptom-checker algorithms, highlighting the critical balance between accessibility and diagnostic precision, and concludes with a framework for integrating these tools into the broader telehealth ecosystem to ensure data privacy and evidence-based reliability.

Keywords: Medical Symptom Checker, Artificial Intelligence, Digital Triage, Machine Learning in Medicine, Clinical Decision Support, Telehealth, Diagnostic Accuracy, Health Informatics.