Background/objectives: The nutrition care process (NCP) is an evidence-based practice framework used in Medical Nutrition Therapy for the prevention, treatment, and management of non-communicable chronic health conditions. This review aimed to explore available artificial intelligence (AI)-integrated technologies across the NCP in dietetic primary care, their uses, and their impacts on the NCP and patient outcomes. Method: Six databases were searched: MEDLINE, Embase, PsycINFO, Scopus, IEEE, and ACM digital library. Eligible studies were published between January 2007 and August 2024 and included human adult studies, AI-integrated technologies in the dietetic primary care setting, and patient-related outcomes. Extracted details focused on participant characteristics, dietitian involvement, and the type of AI system and its application in the NCP. Results: Ninety-seven studies were included. Three different AI systems (image or audio recognition, chatbots, and recommendation systems) were found. These were implemented in web-based or smartphone applications, wearable sensor systems, smart utensils, and software. Most AI-integrated technologies could be incorporated into one or more NCP stages. Seventy-nine studies reported user- or patient-related outcomes, with mixed findings, but all highlighted efficiencies of using AI. Higher patient engagement was observed with Chatbots. Seventeen studies raised concerns encompassing ethics and patient safety. Conclusions: AI systems show promise as a clinical support tool across most stages of the NCP. Whilst they have varying degrees of accuracy, AI demonstrates potential in improving efficiency, supporting personalised nutrition, and enhancing chronic disease management outcomes. Integrating AI education into dietetic training and professional development will be essential to ensure safe and effective use in practice.

Background/Objectives: Dietary protein and carbohydrate affect postprandial glycemia in individuals with type 1 diabetes (T1D). This paper aimed to determine the relationship between the types of dietary protein (Study 1) and carbohydrate (glycemic index; GI, Study 2) and postprandial glycemia. Methods: Two acute randomized crossover trials were conducted in adults with T1D comparing postprandial glycemia for test meals varying by protein type (n = 16 adults; 5 meals: egg, beef, chicken, salmon or whey (all 30 g protein), each served with fried rice (45 g carbohydrate) or GI (n = 8 adults, high or low GI bread, GI 52% vs. 76%) with peanut butter (19 g protein, 30 g fat). Insulin was dosed based on usual individualized insulin: carbohydrate ratio and capillary blood glucose levels (BGL) measured from 30 min pre- to 5 h postprandially in 15–30 min intervals. Results: Study 1: Postprandial glycemia varied over an almost 2-fold range, however responses were highly variable and there were no significant differences between sources (iAUCglucose Chicken: 203 ± 66 mmol·min/L, Egg: 263 ± 100 mmol·min/L, Beef: 309 ± 89 mmol·min/L, Salmon: 338 ± 83 mmol·min/L and Whey: 397 ± 115 mmol·min/L respectively, p > 0.05). Hypoglycemia (≤3.5 mmol/L) occurred at least once per protein type (chicken: 6/16 participants, egg 2/16, beef 3/16, salmon 1/16, whey 2/16). However, there were no statistically significant differences in the risk of hypoglycemia between protein sources (p > 0.05). Study 2: Postprandial glucose response curves were virtually identical for high GI and low GI, and the incremental area under the curve (iAUC) for glucose was not statistically significant after 1 h (p = 0.185), 3 h (p = 0.538) or 5 h (p = 0.694) following the meal. Conclusions: Clinical practice guidelines and insulin dosing algorithms likely do not need to consider differences in protein sources or in GI in the context of a high fat, high protein meals, for individuals with T1D.