Artificial intelligence &amp; clinical nutrition: What the future might have in store

Bond, Ashley; McCay, Kevin D.; Lal, Simon

doi:10.1016/j.clnesp.2023.07.082

Cited by 19 publications

(3 citation statements)

References 63 publications

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“…AI has revolutionized the medical fields, such as medical genetics [37,38], diagnostic investigations [39][40][41], pharmacology [42,43], and clinical nutrition [44,45], especially in providing support for special diet restrictions [24,46]. One of the applications of AI is to help people who need to limit their oxalate intake, which is a common cause of kidney stones.…”

Section: Discussionmentioning

confidence: 99%

Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention

Aiumtrakul,

Thongprayoon,

Arayangkool

et al. 2024

JPM

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Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5–8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.

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Section: Discussionmentioning

confidence: 99%

Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention

Aiumtrakul,

Thongprayoon,

Arayangkool

et al. 2024

JPM

View full text Add to dashboard Cite

show abstract

“…13,15,16 Behind these efforts is artificial intelligence (AI), defined broadly as "techniques in which computers and machines are able to mimic the problem-solving and decision-making capabilities of humans." 17 AI encompasses machine learning, in which systems automatically learn and improve results without explicitly being programmed, and deep learning, which is "a subfield of machine learning that imitates the workings of the human brain to process data and analyze patterns for use in decision making." 17 Whereas typical food logging applications require the user to enter eaten food items using words, image-based food logging apps aim to identify the foods from a photo.…”

Section: Image-based Dietary Assessment Applicationsmentioning

confidence: 99%

“…17 AI encompasses machine learning, in which systems automatically learn and improve results without explicitly being programmed, and deep learning, which is "a subfield of machine learning that imitates the workings of the human brain to process data and analyze patterns for use in decision making." 17 Whereas typical food logging applications require the user to enter eaten food items using words, image-based food logging apps aim to identify the foods from a photo. 18 The primary advantage of using image-based food logs includes a lower burden of data collection, thus increasing user compliance and the generation of more data.…”

Section: Image-based Dietary Assessment Applicationsmentioning

confidence: 99%

Measures of nutrition status and health for weight‐inclusive patient care: A narrative review

Mauldin,

Pignotti,

Gieng

2024

Nut in Clin Prac

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In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non–weight‐related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

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Global Consumer Perception Towards Healthy Foods: Influencing Factors and Current Trends

Mena,

Sîrbu,

Eze

2024

Consumer Perceptions and Food

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Artificial intelligence & clinical nutrition: What the future might have in store

Cited by 19 publications

References 63 publications

Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention

Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention

Measures of nutrition status and health for weight‐inclusive patient care: A narrative review

Global Consumer Perception Towards Healthy Foods: Influencing Factors and Current Trends

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