Authors :
Kavya K A; Shaik Shahid Afrid; Sreekanth Putsala; Bharani Kumar Depuru; Dr. Ilankumaran Kaliamoorthy
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/yy8ce9wu
Scribd :
http://tinyurl.com/ycy5dp2w
DOI :
https://doi.org/10.5281/zenodo.10649891
Abstract :
This study investigates the vital role of highlevel language models (LLMs) in advancing the medical
field and underscores the need for incorporating these
models into a biomedical chatbot framework. The
application of large language models (LLM) in medicine
is both hopeful and concerning, as it can provide answers
with some degree of autonomy. The main objective is to
enhance the availability of crucial medical information
and streamline the extraction of relevant data. By
utilizing cutting-edge LLMs such as GPT 3.5 Turbo,
PaLM2, Llama2 and Mistral 7B, biomedical chatbots are
emerging as a robust platform that enables access to
essential medical information. This study showcases the
efficacy of integrating GPT 3.5 Turbo, PaLM2, and
Gemini Pro into a biomedical chatbot framework,
exhibiting their accuracy and response time. The GPT 3.5
Turbo displayed high accuracy and a swift response time
of 2 seconds, making the team favour PaLM2 as the
second choice, but the difference was not significant and
they were mostly impressed by the performance.
Moreover, performance tests reveal that PaLM2 has an
average response time of around 6.50 seconds, while
Gemini Pro has an average response time of around 8.68
seconds.
Keywords :
Large Language Models, Chatbot, Natural Language Processing, Prompts, Accuracy.
This study investigates the vital role of highlevel language models (LLMs) in advancing the medical
field and underscores the need for incorporating these
models into a biomedical chatbot framework. The
application of large language models (LLM) in medicine
is both hopeful and concerning, as it can provide answers
with some degree of autonomy. The main objective is to
enhance the availability of crucial medical information
and streamline the extraction of relevant data. By
utilizing cutting-edge LLMs such as GPT 3.5 Turbo,
PaLM2, Llama2 and Mistral 7B, biomedical chatbots are
emerging as a robust platform that enables access to
essential medical information. This study showcases the
efficacy of integrating GPT 3.5 Turbo, PaLM2, and
Gemini Pro into a biomedical chatbot framework,
exhibiting their accuracy and response time. The GPT 3.5
Turbo displayed high accuracy and a swift response time
of 2 seconds, making the team favour PaLM2 as the
second choice, but the difference was not significant and
they were mostly impressed by the performance.
Moreover, performance tests reveal that PaLM2 has an
average response time of around 6.50 seconds, while
Gemini Pro has an average response time of around 8.68
seconds.
Keywords :
Large Language Models, Chatbot, Natural Language Processing, Prompts, Accuracy.
