Urology consultants versus large language models: Potentials and hazards for medical advice in urology

Abstract

Background: Current interest surrounding large language models (LLMs) will lead to an increase in their use for medical advice. Although LLMs offer huge potential, they also pose potential misinformation hazards.

Objective: This study evaluates three LLMs answering urology-themed clinical case-based questions by comparing the quality of answers to those provided by urology consultants.

Methods: Forty-five case-based questions were answered by consultants and LLMs (ChatGPT 3.5, ChatGPT 4, Bard). Answers were blindly rated using a six-step Likert scale by four consultants in the categories: 'medical adequacy', 'conciseness', 'coherence' and 'comprehensibility'. Possible misinformation hazards were identified; a modified Turing test was included, and the character count was matched.

Results: Higher ratings in every category were recorded for the consultants. LLMs' overall performance in language-focused categories (coherence and comprehensibility) was relatively high. Medical adequacy was significantly poorer compared with the consultants. Possible misinformation hazards were identified in 2.8% to 18.9% of answers generated by LLMs compared with <1% of consultant's answers. Poorer conciseness rates and a higher character count were provided by LLMs. Among individual LLMs, ChatGPT 4 performed best in medical accuracy (p < 0.0001) and coherence (p = 0.001), whereas Bard received the lowest scores. Generated responses were accurately associated with their source with 98% accuracy in LLMs and 99% with consultants.

Conclusions: The quality of consultant answers was superior to LLMs in all categories. High semantic scores for LLM answers were found; however, the lack of medical accuracy led to potential misinformation hazards from LLM 'consultations'. Further investigations are necessary for new generations.

Keywords: Bard; ChatGPT; artificial intelligence (AI); chatbots; digital health; global health; large language models (LLMs); low‐ and middle‐income countries (LMICs); telehealth; telemedicine; urology.

FIGURE 1

The number of characters per answer by urology consultants and large language models (LLMs; ChatGPT 3.5, Chat GT 4, Bard) for all evaluated categories. Data shown as a scatter dot blot with each point resembling an absolute value. Grey horizontal line = Median. The non‐parametric Mann–Whitney test was used to compare the ratings for individual LLMs to the urology consultants (**** = p < 0.0001).

FIGURE 2

Comparison between urology consultants and large language model (LLMs; ChatGPT 3.5, Chat GPT 4, Bard) for all evaluated categories. Data shown as a scatter dot blot with each point resembling an absolute value. Grey horizontal line = Median. The non‐parametric Mann–Whitney test was used to compare the ratings for individual LLMs to the urology consultants (**** = p < 0.0001; ** = p < 0.01; * = p < 0.05). Cumulative results of ratings for medical adequacy (A), conciseness (B), coherence (C) and comprehensibility (D).