Authors : Ajinkya Mhatre; Sandeep R. Warhade; Omkar Pawar; Sayali Kokate; Samyak Jain; Dr. Emmanuel M

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/yshxnbm4

Scribd : https://tinyurl.com/65ub5wpx

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1964

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

Abstract : Using the application of Large Language Models (LLMs) in healthcare settings, mainly focusing on addressing general illness inquiries through chatbot interfaces. Leveraging the capabilities of LLMs, explore their potential to provide accurate and contextually relevant responses to users seeking information about common health concerns. LLM have the capacity continuously learn and improve from user interaction. Through benchmarking experiments, this paper evaluates the accuracy (61%) of LLM-based chatbots in understanding and responding to user queries related to general illnesses. The findings demonstrate the performance of LLMs against established benchmarks, shedding light on their efficacy in healthcare applications. By examining the intersection of LLM technology and healthcare, this research contributes to advancing the development of intelligent chatbot systems capable of providing reliable and informative support to individuals seeking medical guidance for general health issues.

Keywords : LLM, Healthcare, Chatbot, Benchmark, Accuracy.

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