Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence

This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.PMID:38591653 | DOI:10.1002/ncp.11150

Source: Nutrition in Clinical Practice - April 9, 2024 Category: Nutrition Authors: Kiranjit Atwal Source Type: research