Institute of Fundamental Technological Research

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.

Chatbots,Readability,Cardiovascular health,Oncology