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

Need for User-Friendly Audit Tools: Investigating Dietitians' Use and Requirements of Clinical Documentation Audit Tools

INTRODUCTION

High-quality clinical documentation is critical for ensuring patient safety, enhancing quality of care and outcomes management. Despite the recognised importance of standardised clinical documentation, particularly through the Nutrition Care Process (NCP) and its associated terminology, studies indicate flaws in current practices that may have negative impacts on patient outcomes and interprofessional communication. Regular auditing of clinical documentation could help in improving clinical documentation quality. Despite the availability of validation studies of clinical documentation audit tools, information on their use and dietitians' requirements is lacking. This study aimed to investigate the dietitians' use of clinical documentation audit tools internationally and to learn about their requirements for these tools.

METHODS

A quantitative cross-sectional online survey was conducted in October 2021 using a newly developed and pretested 26-item questionnaire among dietitians identified through convenience sampling. The survey, developed through a multi-step approach including expert review and pretesting, collected data on clinical documentation audit tool use, purpose of auditing, preferred tool formats, and perceived enablers and barriers. Descriptive statistics and inferential analyses were applied to compare current practices and desired future applications of current auditors and non-auditors.

RESULTS

A total of 154 respondents from 16 countries completed the survey, with more than half working in patient-related fields. Fifty-three percent indicated that clinical documentation audits were conducted in their workplaces Audit purpose was primarily improving clinical documentation quality, reinforcing NCP understanding, and enhancing clarity, with significant differences observed between current and desired uses regarding result comparability and quality reporting (p < 0.001). Key enablers included management support, education/training, time, and helpful manuals, while barriers included lack of knowledge, time constraints, and insufficient training. Auditors used the tools mainly in paper format (33%) or as a text processing/spreadsheet file (26%), with 51% preferring a web application in the future. Additional requirements included further manual development, benchmarking capabilities, and cross-cultural adaptations.

CONCLUSION

The process of clinical documentation auditing is not well established in the nutrition and dietetics community but has the potential to enhance clinical documentation quality. Key requirements include best practices for clinical documentation auditing processes, educational resources and user-friendly, web-based tools. Future research should validate clinical documentation audit tools across different settings and explore barriers to clinical documentation auditing as well as evaluating the use of artificial intelligence for clinical documentation auditing, ensuring improved clinical documentation quality translates to better patient care.

Healthy Diet Consumption Among Chronic Pain Populations: A Concept Analysis

PURPOSE

Given emerging evidence that healthy dietary interventions are effective long-term strategies for managing chronic pain, this review aimed to define, elucidate, and describe the concept of a healthy diet in the context of chronic pain populations.

DESIGN

We used Walker and Avant's concept analysis method.

METHOD

PubMed, Embase, CINAHL Plus with full-text, and PsycINFO databases were searched to identify relevant peer-reviewed primary articles on diet and chronic pain, published from June 2013 to June 2024. Key search terms included "diet" AND "chronic pain or pain."

RESULTS

Twenty-eight primary articles met our eligibility criteria following full-text reviews. In chronic pain, healthy diet attributes (i.e., nutrient density, anti-inflammation, and anti-oxidation) caused by antecedents (i.e., diet and pain assessments) result in consequences like reduced pain intensity and improved quality of life. Therefore, a healthy diet in chronic pain consists of nutrient-dense foods (fruits, vegetables, healthy fats and low calories) that possess strong anti-inflammatory and antioxidant properties, which are essential for optimizing health, alleviating pain, and enhancing overall quality of life.

CONCLUSION

A healthy diet is essential for pain relief and improving the quality of life in individuals with chronic pain.

IMPLICATIONS

Healthcare providers should incorporate individualized culturally appropriate dietary preferences, food intolerance, and food allergy alternatives in dietary interventions. Also, there is a need for tailored dietary interventions for individuals living with chronic pain. Future studies should explore mechanisms through which diet affects pain outcomes.

Assessing the Links Between Artificial Intelligence and Precision Nutrition

PURPOSE OF REVIEW

To conduct an overview of the potentialities of artificial intelligence in precision nutrition.

RECENT FINDINGS

A keyword co-occurrence analysis of 654 studies on artificial intelligence (AI) and precision nutrition (PN) highlighted the potential of AI techniques like Random Forest and Gradient Boosting in improving personalized dietary recommendations. These methods address gastrointestinal symptoms, weight management, and cardiometabolic markers, especially when incorporating data on gut microbiota. Despite its promise, challenges like data privacy, bias, and ethical concerns remain. AI must complement healthcare professionals, necessitating clear guidelines, robust governance, and ongoing research to ensure safe and effective applications. The integration of AI into PN enables highly personalized dietary recommendations by accounting for metabolic variability, genetics, and microbiome data. AI-driven strategies show potential in managing conditions like obesity and diabetes through accurate predictions of individual dietary responses. However, ethical, regulatory, and practical challenges must be addressed to ensure safe, equitable, and effective application of AI in nutrition.

Still a Long Way to Go, the Potential of ChatGPT in Personalized Dietary Prescription, From a Perspective of a Clinical Dietitian

OBJECTIVE

Prominent large language models, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have shown promising implementation in the field of nutrition. Special care should be taken when using ChatGPT to prescribe protein-restricted diets for kidney-impaired patients. The objective of the current study is to simulate a chronic kidney disease (CKD) patient and evaluate the capabilities of ChatGPT in the context of dietary prescription, with a focus on protein contents of the diet.

METHODS

We simulated a scenario involving a CKD patient and replicated a clinical counseling session that covered general dietary principles, dietary assessment, energy and protein recommendation, dietary prescription, and diet customization based on dietary culture. To confirm the results derived from our qualitative observations, 10 colleagues were recruited and provided with identical dietary prescription prompts to run the process again. The actual energy and protein levels of the given meal plans were recorded and the difference from the targets were compared.

RESULTS

ChatGPT provides general principles overall aligning with best practices. The recommendations for energy and protein requirements of CKD patients were tailored and satisfactory. It failed to prescribe a reliable diet based on the target energy and protein requirements. For the quantitative analysis, the prescribed energy levels were generally lower than the targets, ranging from -28.9% to -17.0%, and protein contents were tremendously higher than the targets, ranging from 59.3% to 157%.

CONCLUSION

ChatGPT is competent in offering generic dietary advice, giving satisfactory nutrients recommendations and adapting cuisines to different cultures but failed to prescribe nutritionally accurate dietary plans for CKD patients. At present, patients with strict protein and other particular nutrient restrictions are not recommended to rely on the dietary plans prescribed by ChatGPT to avoid potential health risks.

Evaluating ChatGPT's Multilingual Performance in Clinical Nutrition Advice Using Synthetic Medical Text: Insights from Central Asia

BACKGROUND

Although large language models like ChatGPT-4 have demonstrated competency in English, their performance for minority groups speaking underrepresented languages, as well as their ability to adapt to specific sociocultural nuances and regional cuisines, such as those in Central Asia (for example, Kazakhstan), still requires further investigation.

OBJECTIVES

To evaluate and compare the effectiveness of the ChatGPT-4 system in providing personalized, evidence-based nutritional recommendations in English, Kazakh, and Russian in Central Asia.

METHODS

This study was conducted from 15 May to 31 August, 2023. On the basis of 50 mock patient profiles, ChatGPT-4 generated dietary advice, and responses were evaluated for personalization, consistency, and practicality using a 5-point Likert scale. To identify significant differences between the 3 languages, the Kruskal-Wallis test was conducted. Additional pairwise comparisons for each language were carried out using the post hoc Dunn's test.

RESULTS

ChatGPT-4 showed a moderate level of performance in each category for English and Russian languages, whereas in Kazakh language, outputs were unsuitable for evaluation. The scores for English, Russian, and Kazakh were as follows: for personalization, 3.32 ± 0.46, 3.18 ± 0.38, and 1.01 ± 0.06; for consistency, 3.48 ± 0.43, 3.38 ± 0.39, and 1.09 ± 0.18; and for practicality, 3.25 ± 0.41, 3.37 ± 0.38, and 1.07 ± 0.15, respectively. The Kruskal-Wallis test indicated statistically significant differences in ChatGPT-4's performance across the 3 languages (P < 0.001). Subsequent post hoc analysis using Dunn's test showed that the performance in both English and Russian was significantly different from that in Kazakh.

CONCLUSIONS

Our findings show that, despite using identical prompts across 3 distinct languages, the ChatGPT-4's capability to produce sensible outputs is limited by the lack of training data in non-English languages. Thus, a customized large language model should be developed to perform better in underrepresented languages and to take into account specific local diets and practices.

Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact

Personalized nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products, or services to support both prevention and treatment of nutrition-related conditions and to improve individual health using genetic, phenotypic, medical, nutritional, and other pertinent information. However, current approaches have yielded limited scientific success in improving diets or in mitigating diet-related conditions. In addition, PN currently caters to a specific subgroup of the population rather than having a widespread impact on diet and health at a population level. Addressing these challenges requires integrating traditional biomedical and dietary assessment methods with psycho-behavioral, and novel digital and diagnostic methods for comprehensive data collection, which holds considerable promise in alleviating present PN shortcomings. This comprehensive approach not only allows for deriving personalized goals ("what should be achieved") but also customizing behavioral change processes ("how to bring about change"). We herein outline and discuss the concept of "Adaptive Personalized Nutrition Advice Systems," which blends data from 3 assessment domains: 1) biomedical/health phenotyping; 2) stable and dynamic behavioral signatures; and 3) food environment data. Personalized goals and behavior change processes are envisaged to no longer be based solely on static data but will adapt dynamically in-time and in-situ based on individual-specific data. To successfully integrate biomedical, behavioral, and environmental data for personalized dietary guidance, advanced digital tools (e.g., sensors) and artificial intelligence-based methods will be essential. In conclusion, the integration of both established and novel static and dynamic assessment paradigms holds great potential for transitioning PN from its current focus on elite nutrition to a widely accessible tool that delivers meaningful health benefits to the general population.

Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval

Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, and facilitating medical education. Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. In this paper, we propose to employ the Registered Dietitian (RD) exam to conduct a standard and comprehensive evaluation of state-of-the-art LLMs, GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, assessing both accuracy and consistency in nutrition queries. Our evaluation includes 1050 RD exam questions encompassing several nutrition topics and proficiency levels. In addition, for the first time, we examine the impact of Zero-Shot (ZS), Chain of Thought (CoT), Chain of Thought with Self Consistency (CoT-SC), and Retrieval Augmented Prompting (RAP) on both accuracy and consistency of the responses. Our findings revealed that while these LLMs obtained acceptable overall performance, their results varied considerably with different prompts and question domains. GPT-4o with CoT-SC prompting outperformed the other approaches, whereas Gemini 1.5 Pro with ZS recorded the highest consistency. For GPT-4o and Claude 3.5, CoT improved the accuracy, and CoT-SC improved both accuracy and consistency. RAP was particularly effective for GPT-4o to answer Expert level questions. Consequently, choosing the appropriate LLM and prompting technique, tailored to the proficiency level and specific domain, can mitigate errors and potential risks in diet and nutrition chatbots.

Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence

BACKGROUND

Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at the intricate connection between food and health in both an individual and a community context. AI also helps in tracing and offering solutions in dietary assessment, personalized and clinical nutrition, as well as disease prediction and management, such as cardiovascular diseases, diabetes, cancer, and obesity. This review aims to investigate and assess the different applications and roles of AI in nutrition and research and understand its potential future impact.

METHODS

We used PubMed, Scopus, Web of Science, Google Scholar, and EBSCO databases for our search.

RESULTS

Our findings indicate that AI is reshaping the field of nutrition in ways that were previously unimaginable. By enhancing how we assess diets, customize nutrition plans, and manage complex health conditions, AI has become an essential tool. Technologies like machine learning models, wearable devices, and chatbot applications are revolutionizing the accuracy of dietary tracking, making it easier than ever to provide tailored solutions for individuals and communities. These innovations are proving invaluable in combating diet-related illnesses and encouraging healthier eating habits. One breakthrough has been in dietary assessment, where AI has significantly reduced errors that are common in traditional methods. Tools that use visual recognition, deep learning, and mobile applications have made it possible to analyze the nutrient content of meals with incredible precision.

CONCLUSIONS

Moving forward, collaboration between tech developers, healthcare professionals, policymakers, and researchers will be essential. By focusing on high-quality data, addressing ethical challenges, and keeping user needs at the forefront, AI can truly revolutionize nutrition science. The potential is enormous. AI is set to make healthcare not only more effective and personalized but also more equitable and accessible for everyone.

Comparison of the Accuracy, Completeness, Reproducibility, and Consistency of Different AI Chatbots in Providing Nutritional Advice: An Exploratory Study

Background: The use of artificial intelligence (AI) chatbots for obtaining healthcare advice is greatly increased in the general population. This study assessed the performance of general-purpose AI chatbots in giving nutritional advice for patients with obesity with or without multiple comorbidities. Methods: The case of a 35-year-old male with obesity without comorbidities (Case 1), and the case of a 65-year-old female with obesity, type 2 diabetes mellitus, sarcopenia, and chronic kidney disease (Case 2) were submitted to 10 different AI chatbots on three consecutive days. Accuracy (the ability to provide advice aligned with guidelines), completeness, and reproducibility (replicability of the information over the three days) of the chatbots' responses were evaluated by three registered dietitians. Nutritional consistency was evaluated by comparing the nutrient content provided by the chatbots with values calculated by dietitians. Results: Case 1: ChatGPT 3.5 demonstrated the highest accuracy rate (67.2%) and Copilot the lowest (21.1%). ChatGPT 3.5 and ChatGPT 4.0 achieved the highest completeness (both 87.3%), whereas Gemini and Copilot recorded the lowest scores (55.6%, 42.9%, respectively). Reproducibility was highest for Chatsonic (86.1%) and lowest for ChatGPT 4.0 (50%) and ChatGPT 3.5 (52.8%). Case 2: Overall accuracy was low, with no chatbot achieving 50% accuracy. Completeness was highest for ChatGPT 4.0 and Claude (both 77.8%), and lowest for Copilot (23.3%). ChatGPT 4.0 and Pi Ai showed the lowest reproducibility. Major inconsistencies regarded the amount of protein recommended by most chatbots, which suggested simultaneously to both reduce and increase protein intake. Conclusions: General-purpose AI chatbots exhibited limited accuracy, reproducibility, and consistency in giving dietary advice in complex clinical scenarios and cannot replace the work of an expert dietitian.

Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management-A State-of-the-Art Narrative Review

The global rise in obesity underscores the need for effective weight management strategies that address individual metabolic and hormonal variability, moving beyond the simplistic "calories in, calories out" model. Body types-ectomorph, mesomorph, and endomorph-provide a framework for understanding the differences in fat storage, muscle development, and energy expenditure, as each type responds uniquely to caloric intake and exercise. Variability in weight outcomes is influenced by factors such as genetic polymorphisms and epigenetic changes in hormonal signaling pathways and metabolic processes, as well as lifestyle factors, including nutrition, exercise, sleep, and stress. These factors impact the magnitude of lipogenesis and myofibrillar protein synthesis during overfeeding, as well as the extent of lipolysis and muscle proteolysis during caloric restriction, through complex mechanisms that involve changes in the resting metabolic rate, metabolic pathways, and hormonal profiles. Precision approaches, such as nutrigenomics, indirect calorimetry, and artificial-intelligence-based strategies, can potentially leverage these insights to create individualized weight management strategies aligned with each person's unique metabolic profile. By addressing these personalized factors, precision nutrition offers a promising pathway to sustainable and effective weight management outcomes. The main objective of this review is to examine the metabolic and hormonal adaptations driving variability in weight management outcomes and explore how precision nutrition can address these challenges through individualized strategies.

Nourishing sustainability: Clinical nutrition's impact on climate change

INTRODUCTION

Preserving our planet's delicate balance is not merely a choice but a responsibility we owe to future generations, ensuring equitable, and sustainable world. The 2030 Agenda for Sustainable Development, adopted by all United Nations (UN) member states in 2015, offers a collective vision for global peace and prosperity. Central to this agenda are the 17 Sustainable Development Goals (SDGs), which call for unified action from all nations, irrespective of their developmental status, under a global partnership.

METHOD

This paper examines the SDGs' framework and its applicability to clinical nutrition (CN). We provide a comprehensive narrative review relating to the integration of SDGs in CN practices. Whilst recognising the importance of the SDG framework we elected to focus specifically upon the environmental aspects of CN care.

RESULTS

The analysis revealed that the SDGs provide a robust framework for promoting sustainability in clinical nutrition. Key findings highlight the interconnection between health improvement and other SDGs, such as poverty reduction and climate action. Effective CN practices contribute to broader sustainable development by ensuring better health outcomes, which in turn support economic growth and reduce inequalities. Additionally, strategies in CN that focus on reducing waste and improving resource efficiency align with environmental sustainability goals.

CONCLUSION

The 17 SDGs offer a comprehensive guide for advancing sustainability across various fields, including clinical nutrition. By adopting these goals, healthcare providers can implement holistic strategies that not only improve health outcomes but also support broader efforts to achieve global sustainability.

Using augmented intelligence to improve long term outcomes

PURPOSE OF REVIEW

For augmented intelligence (AI) tools to realize their potential, critical care clinicians must ensure they are designed to improve long-term outcomes. This overview is intended to align professionals with the state-of-the art of AI.

RECENT FINDINGS

Many AI tools are undergoing preliminary assessment of their ability to support the care of survivors and their caregivers at multiple time points after intensive care unit (ICU) discharge. The domains being studied include early identification of deterioration (physiological, mental), management of impaired physical functioning, pain, sleep and sexual dysfunction, improving nutrition and communication, and screening and treatment of cognitive impairment and mental health disorders.Several technologies are already being marketed and many more are in various stages of development. These technologies mostly still require clinical trials outcome testing. However, lacking a formal regulatory approval process, some are already in use.

SUMMARY

Plans for long-term management of ICU survivors must account for the development of a holistic follow-up system that incorporates AI across multiple platforms. A tiered post-ICU screening program may be established wherein AI tools managed by ICU follow-up clinics provide appropriate assistance without human intervention in cases with less pathology and refer severe cases to expert treatment.

Connecting precision nutrition with the Food is Medicine approach

Two initiatives are reshaping how we can approach and address the persistent and widely prevalent challenge of malnutrition, the leading global risk factor for morbidity and mortality. First is the focus on precision nutrition to identify inter- and intra-individual variation in our responses to diet, and its determinants. Second is the Food is Medicine (FIM) approach, an umbrella term for programs and services that link nutrition and health through the provision of food (e.g., tailored meals, produce prescriptions) and access to healthcare services. This article outlines how interventions and programs using FIM can synergize with precision nutrition approaches to make individual- or population-level tailored nutrition accessible and affordable, help to reduce the risk of metabolic diseases, and improve quality of life.

Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint

The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia's Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: -1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.

The role of diet in cancer: the potential of shaping public policy and clinical outcomes in the UK

Cancer universally represents one of the largest public health concerns, substantially contributing to global disease burden and mortality. The multifaceted interplay of environmental and genetic factors in the disease aetiology and progression has required comprehensive research to elucidate modifiable elements which can reduce the risk of incidence and improve prognosis. Among these factors, diet and nutrition have emerged as the most fundamental with a significant potential for influence and effect. Nutrition is not only an essential part of human survival, but also a vital determinant of overall health. Certain dietary requirements are necessary to support normal physiology. This includes individualised levels of macronutrients (proteins, carbohydrates and fats) and specific micronutrients (vitamins and minerals). Extensive research has demonstrated that diet plays a role in cancer pathogenesis at the genetic, epigenetic and cellular level. Therefore, its potential as a modifiable determinant of cancer pathogenesis for the purpose of prevention and improving management of disease must be further explored and implemented. The ability to influence cancer incidence and outcomes through dietary changes is underutilised in clinical practice and insufficiently recognised among the general public, healthcare professionals and policy-makers. Dietary changes offer the opportunity for autonomy and control over individuals health outcomes. Research has revealed that particular dietary components, as well as cultural behaviours and epidemiological patterns may act as causative or protective factors in cancer development. This review aims to comprehensively synthesise this research to further explore how to best utilise this knowledge within the community and clinical environment for more effective cancer prevention and therapeutic strategies. The identified key areas for improvement include the development of more specific, widely accepted guidelines, promoting increased involvement of dieticians within cancer multidisciplinary teams, enhancing nutritional education for healthcare professionals and exploring the potential implementation of personalised nutrition tools. A greater understanding of the complex interactions between diet and cancer will facilitate informed clinical interventions and public health policies to reduce global cancer burden and improve care for cancer patients and survivors.

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

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.

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review

Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

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

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.