Authors : Dr. Maya Eapen; Anboli M.; Gowtham V.; Vasantha Kumar V.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2s3jjvfd

Scribd : https://tinyurl.com/pkcz2k63

DOI : https://doi.org/10.38124/ijisrt/26mar760

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial intelligence has rapidly transformed many domains, including digital healthcare systems that assist individuals in monitoring and improving their wellbeing. This paper presents a Health Companion powered by a Large Language Model (LLM), designed to provide personalized health guidance through conversational interaction. Unlike traditional health applications that rely mainly on manual data input and fixed rule-based alerts, the proposed system allows users to communicate using natural language. By interpreting contextual information related to a user's lifestyle, symptoms, and health patterns, the system can deliver more relevant and personalized recommendations. The proposed framework integrates multiple components including data collection, preprocessing, anomaly detection, predictive analytics, and domain-specific model adaptation. These components work together to generate meaningful insights that help users understand potential health risks and adopt preventive care practices. The system focuses on privacypreserving interaction and avoids unnecessary storage of sensitive medical information while still providing effective assistance. Experimental evaluation demonstrates that the Health Companion LLM improves accessibility to basic health guidance and encourages proactive health management. The proposed approach highlights the potential of conversational AI systems in supporting personalized healthcare monitoring and early preventive interventions.

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