AI Medical Compendium Journal:
Advances in nutrition (Bethesda, Md.)

Showing 1 to 6 of 6 articles

Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.

Advances in nutrition (Bethesda, Md.)
BACKGROUND: Malnutrition is a critical complication among cancer patients, affecting up to 80% of individuals depending on cancer type, stage, and treatment. Artificial Intelligence (AI) has emerged as a promising tool in healthcare, with potential a...

A Scoping Review of Artificial Intelligence for Precision Nutrition.

Advances in nutrition (Bethesda, Md.)
With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is needed. This scoping review examines: 1) the current landscape, including publication venues...

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Advances in nutrition (Bethesda, Md.)
Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and other medical complications. Advances in technology that enable continuous biological data collection...

Artificial Intelligence in Malnutrition: A Systematic Literature Review.

Advances in nutrition (Bethesda, Md.)
Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term comp...

Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review.

Advances in nutrition (Bethesda, Md.)
Dietary assessment can be crucial for the overall well-being of humans and, at least in some instances, for the prevention and management of chronic, life-threatening diseases. Recall and manual record-keeping methods for food-intake monitoring are a...

Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.

Advances in nutrition (Bethesda, Md.)
The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can h...