The role of Artificial Intelligence in nutritional assessment in clinical practice

Nutrition plays a crucial role in clinical practice, impacting immune function, disease prevention, and treatment. However, assessing a patient’s nutritional status remains challenging due to individual differences and the limitations of traditional methods like dietary recalls and food diaries, which can be subjective and time-consuming. Artificial Intelligence (AI), particularly machine learning, is emerging as a powerful tool to enhance nutritional assessments. AI can quickly and accurately analyze large datasets, enabling both patients and clinicians to track and manage dietary intake, blood sugar levels, and other health metrics through apps and wearable devices. Despite its promise, AI in nutrition faces challenges such as data accuracy, potential misdiagnoses, and ethical concerns related to data privacy and security. Nonetheless, as technology advances, AI is set to play an increasingly significant role in nutrition diagnostics and healthcare, offering new possibilities for personalized and efficient nutritional management.

Authors

Publication Details

Journal
World Nutrition Journal
Published
2025-02-28
DOI
https://doi.org/10.25220/wnj.v08.i2.000i
Primary Topic
Body Composition Measurement Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

The role of Artificial Intelligence in nutritional assessment in clinical practice

Pittara Pansawira
World Nutrition Journal
Body Composition Measurement Techniques
article

The role of Artificial Intelligence in nutritional assessment in clinical practice

Pittara Pansawira
article en

Abstract

Nutrition plays a crucial role in clinical practice, impacting immune function, disease prevention, and treatment. However, assessing a patient’s nutritional status remains challenging due to individual differences and the limitations of traditional methods like dietary recalls and food diaries, which can be subjective and time-consuming. Artificial Intelligence (AI), particularly machine learning, is emerging as a powerful tool to enhance nutritional assessments. AI can quickly and accurately analyze large datasets, enabling both patients and clinicians to track and manage dietary intake, blood sugar levels, and other health metrics through apps and wearable devices. Despite its promise, AI in nutrition faces challenges such as data accuracy, potential misdiagnoses, and ethical concerns related to data privacy and security. Nonetheless, as technology advances, AI is set to play an increasingly significant role in nutrition diagnostics and healthcare, offering new possibilities for personalized and efficient nutritional management.

World Nutrition JournalVol. 8(i2)
Openalex Percentile: Top 95%
Body Composition Measurement Techniques
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