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VOLUME 28 , ISSUE 6 ( June, 2024 ) > List of Articles

Original Article

End-of-life Care Patient Information Leaflets—A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini

Prakash G Gondode, Puneet Khanna, Pradeep Sharma, Sakshi Duggal, Neha Garg

Keywords : Artificial intelligence, ChatGPT, End-of-life care, Gemini, Intensive care, Palliative care, Patient education, Readability

Citation Information : Gondode PG, Khanna P, Sharma P, Duggal S, Garg N. End-of-life Care Patient Information Leaflets—A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini. Indian J Crit Care Med 2024; 28 (6):561-568.

DOI: 10.5005/jp-journals-10071-24725

License: CC BY-NC 4.0

Published Online: 31-05-2024

Copyright Statement:  Copyright © 2024; The Author(s).


Abstract

Background: End-of-life care (EOLC) is a critical aspect of healthcare, yet accessing reliable information remains challenging, particularly in culturally diverse contexts like India. Objective: This study investigates the potential of artificial intelligence (AI) in addressing the informational gap by analyzing patient information leaflets (PILs) generated by AI chatbots on EOLC. Methodology: Using a comparative research design, PILs generated by ChatGPT and Google Gemini were evaluated for readability, sentiment, accuracy, completeness, and suitability. Readability was assessed using established metrics, sentiment analysis determined emotional tone, accuracy, and completeness were rated by subject experts, and suitability was evaluated using the Patient Education Materials Assessment Tool (PEMAT). Results: Google Gemini PILs exhibited superior readability and actionability compared to ChatGPT PILs. Both conveyed positive sentiments and high levels of accuracy and completeness, with Google Gemini PILs showing slightly lower accuracy scores. Conclusion: The findings highlight the promising role of AI in enhancing patient education in EOLC, with implications for improving care outcomes and promoting informed decision-making in diverse cultural settings. Ongoing refinement and innovation in AI-driven patient education strategies are needed to ensure compassionate and culturally sensitive EOLC.


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