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Presentation Date / Time
Submission Status
Submitted
Abstract
Abstract Title
Patient-directed information on Wilms tumour from artificial intelligence large language models: quality assessment and appraisal
Presentation Type
Moderated Poster Abstract
Manuscript Type
Clinical Research
Abstract Category *
AI in Urology
Author's Information
Number of Authors (including submitting/presenting author) *
4
No more than 10 authors can be listed (as per the Good Publication Practice (GPP) Guidelines).
Please ensure the authors are listed in the right order.
Country
Australia
Co-author 1
Jordan Santucci santuccijordan@gmail.com Grampians Health Ballarat Australia *
Co-author 2
Peter Stapleton peter.stapleton@outlook.com Grampians Health Ballarat Australia -
Co-author 3
Thomas Cundy thomas.cundy@adelaide.edu.au Flinders Medical Centre Adelaide Australia -
Co-author 4
Niranjan Sathianathen niranjan19@gmail.com Austin Health Melbourne Australia -
Co-author 5
Co-author 6
Co-author 7
Co-author 8
Co-author 9
Co-author 10
Co-author 11
Co-author 12
Co-author 13
Co-author 14
Co-author 15
Co-author 16
Co-author 17
Co-author 18
Co-author 19
Co-author 20
Abstract Content
Introduction
Wilms tumor is the third most common solid malignancy in childhood, typically occurring in children younger than five years old. Families increasingly conduct their own independent internet search for information to better understanding a diagnosis. The reliability and quality of this information for patients and families has not previously been formally assessed. Artificial Intelligence (AI) is not new to medicine and health care, but public-facing AI driven search engines and chatbots have become extremely popular since the launch of ChatGPT in November 2022. We aimed to assess the ability of large language model AI chatbots to deliver quality and understandable information on Wilms tumours to patients and their families.
Materials and Methods
Google trends were used to evaluate the most asked questions related to Wilms tumour. Four AI chatbots (ChatGPT version 3.5, Perplexity, Chat Sonic, and Bing AI) were then used to assess these questions and their responses reviewed. Validated instruments were used to assess the quality (DISCERN instrument from 1 low to 5 high), understandability and actionability (PEMAT, from 0 to 100%), the reading level of the information and whether there was misinformation compared to guidelines (5-point Likert scale).
Results
All AI chat bots provided a high level of patient health information with a median DISCERN score of 4 (IQR 3-5). Additionally, there was little to no misinformation in outputs with a median of 1 (IQR 1-1). The median word count per output from the AIs was 275 (IQR 156 – 322), with an advanced ease of reading level comparable to a high school or college student, median Flesch-Kincaid Readability level of 46.7 (IQR 41.1 – 52.2). The overall PEMAT actionability was poor with a median of 40% (40-65), while the PEMAT understandability of the AI chatbot outputs was high, 83% (IQR 75 – 91.2).
Conclusions
AI chatbots provide generalised, understandable and accurate information regarding Wilms tumour and can be used reliably to help inform patients and families seeking further information. However, much of the information is reliant on medical professionals and not easily actionable by consumers but may act as a guide to help with discussions and understanding treatments.
Keywords
Artificial intelligence, chatbot, large language model, Wilms tumour, nephroblastoma, patient-centred care, patient education
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1836
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