AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat
3
Issued Date
2023-10-01
Resource Type
eISSN
20397283
Scopus ID
2-s2.0-85175418287
Journal Title
Clinics and Practice
Volume
13
Issue
5
Start Page
1160
End Page
1172
Rights Holder(s)
SCOPUS
Bibliographic Citation
Clinics and Practice Vol.13 No.5 (2023) , 1160-1172
Suggested Citation
Qarajeh A., Tangpanithandee S., Thongprayoon C., Suppadungsuk S., Krisanapan P., Aiumtrakul N., Garcia Valencia O.A., Miao J., Qureshi F., Cheungpasitporn W. AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat. Clinics and Practice Vol.13 No.5 (2023) , 1160-1172. 1172. doi:10.3390/clinpract13050104 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/90999
Title
AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat
Other Contributor(s)
Abstract
Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models—ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat—were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook’s recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.