OpenLB
Open Library of Bioscience

BACKGROUND: Chatbots using the Large Language Model (LLM) generate human responses to questions from all categories. Due to staff shortages in healthcare systems, patients waiting for an appointment increasingly use chatbots to get information about their condition. Given the number of chatbots currently available, assessing the responses they generate is essential.
METHODS: Five chatbots with free access were selected (Gemini, Microsoft Copilot, PiAI, ChatGPT, ChatSpot) and blinded using letters (A, B, C, D, E). Each chatbot was asked questions about cardiology, oncology, and psoriasis. Responses were compared to guidelines from the European Society of Cardiology, American Academy of Dermatology and American Society of Clinical Oncology. All answers were assessed using readability scales (Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level and Dale-Chall Score). Using a 3-point Likert scale, two independent medical professionals assessed the compliance of the responses with the guidelines.
RESULTS: A total of 45 questions were asked of all chatbots. Chatbot C gave the shortest answers, 7.0 (6.0 - 8.0), and Chatbot A the longest 17.5 (13.0 - 24.5). The Flesch Reading Ease Scale ranged from 16.3 (12.2 - 21.9) (Chatbot D) to 39.8 (29.0 - 50.4) (Chatbot A). Flesch-Kincaid Grade Level ranged from 12.5 (10.6 - 14.6) (Chatbot A) to 15.9 (15.1 - 17.1) (Chatbot D). Gunning Fog Scale Level ranged from 15.77 (Chatbot A) to 19.73 (Chatbot D). Dale-Chall Score ranged from 10.3 (9.3 - 11.3) (Chatbot A) to 11.9 (11.5 - 12.4) (Chatbot D).
CONCLUSION: This study indicates that chatbots vary in length, quality, and readability. They answer each question in their own way, based on the data they have pulled from the web. Reliability of the responses generated by chatbots is high. This suggests that people who want information from a chatbot need to be careful and verify the answers they receive, particularly when they ask about medical and health aspects.