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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Chatbots vs andrologists: Testing 25 clinical cases.

Ophélie Perrot¹, Aurelie Schirmann¹, Adrien Vidart¹

¹Foch Hospital, Urology department, Suresnes, France.

The French Journal of Urology

|April 10, 2024

Summary

This summary is machine-generated.

AI chatbots show potential in medicine, but human experts still outperform them in andrology cases. Further research is needed to determine the reliable integration of these AI language models into clinical practice.

Keywords:

Andrology Artificial intelligence Clinical reasoning Natural language processing

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Area of Science:

Medical Artificial Intelligence
Andrology
Clinical Decision Support Systems

Background:

The rapid advancement of AI language models necessitates an evaluation of their role in healthcare.
The reliability of AI in clinical settings, particularly in specialized fields like andrology, remains undefined.

Purpose of the Study:

To compare the accuracy and reliability of responses generated by AI chatbots versus human andrologists for clinical cases.
To assess the potential of AI language models in supporting andrology practice.

Main Methods:

A comparative analysis was conducted on responses to 25 andrology clinical cases.
Responses from 32 experts, 18 residents, and three AI chatbots (ChatGPT v3.5, v4, and Bard) were evaluated.
A Likert scale (0-2) was used to score responses based on current medical recommendations.

Main Results:

Human experts achieved the highest mean score, outperforming all AI chatbots.
ChatGPT v4 demonstrated superior performance compared to ChatGPT v3.5 and Bard.
Residents' performance was better than Bard but lower than ChatGPT v3.5 and v4, and experts.

Conclusions:

AI chatbots show promise for medical applications but require further investigation.
Additional studies are essential to guide the integration of AI chatbots into clinical andrology practice.