Comparative Analysis of Accuracy, Readability, Sentiment, and Actionability: Artificial Intelligence Chatbots (ChatGPT and Google Gemini) versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery
Open Access
- 19 August 2024
- journal article
- Published by White Rose University Press in British and Irish Orthoptic Journal
- Vol. 20 (1) , 183-192
Abstract
Eye surgeries often evoke strong negative emotions in patients, including fear and anxiety. Patient education material plays a crucial role in informing and empowering individuals. Traditional sources of medical information may not effectively address individual patient concerns or cater to varying levels of understanding. This study aims to conduct a comparative analysis of the accuracy, completeness, readability, tone, and understandability of patient education material generated by AI chatbots versus traditional Patient Information Leaflets (PILs), focusing on local anesthesia in eye surgery. Expert reviewers evaluated responses generated by AI chatbots (ChatGPT and Google Gemini) and a traditional PIL (Royal College of Anaesthetists’ PIL) based on accuracy, completeness, readability, sentiment, and understandability. Statistical analyses, including ANOVA and Tukey HSD tests, were conducted to compare the performance of the sources. Readability analysis showed variations in complexity among the sources, with AI chatbots offering simplified language and PILs maintaining better overall readability and accessibility. Sentiment analysis revealed differences in emotional tone, with Google Gemini exhibiting the most positive sentiment. AI chatbots demonstrated superior understandability and actionability, while PILs excelled in completeness. Overall, ChatGPT showed slightly higher accuracy (scores expressed as mean ± standard deviation) (4.71 ± 0.5 vs 4.61 ± 0.62) and completeness (4.55 ± 0.58 vs 4.47 ± 0.58) compared to Google Gemini, but PILs performed best (4.84 ± 0.37 vs 4.88 ± 0.33) in terms of both accuracy and completeness (p-value for completeness <0.05). AI chatbots show promise as innovative tools for patient education, complementing traditional PILs. By leveraging the strengths of both AI-driven technologies and human expertise, healthcare providers can enhance patient education and empower individuals to make informed decisions about their health and medical care.Keywords
This publication has 16 references indexed in Scilit:
- Detection of emotion by text analysis using machine learningFrontiers in Psychology, 2023
- Fear and Anxiety Associated with Cataract Surgery Under Local Anesthesia in Adults: A Systematic ReviewPsychology Research and Behavior Management, 2021
- Causability and explainability of artificial intelligence in medicineWIREs Data Mining and Knowledge Discovery, 2019
- How scientists can take the lead in establishing ethical practices for social media researchJournal of the American Medical Informatics Association, 2019
- Clinical Decision Support in the Era of Artificial IntelligenceJAMA, 2018
- Development of the Patient Education Materials Assessment Tool (PEMAT): A new measure of understandability and actionability for print and audiovisual patient informationPatient Education and Counseling, 2014
- Predictors of eHealth Usage: Insights on The Digital Divide From the Health Information National Trends Survey 2012Journal of Medical Internet Research, 2014
- Developing a quality criteria framework for patient decision aids: online international Delphi consensus processBMJ, 2006
- The role of pictures in improving health communication: A review of research on attention, comprehension, recall, and adherencePatient Education and Counseling, 2006
- Why organizations continue to create patient information leaflets with readability and usability problems: an exploratory studyHealth Education Research, 2004