2026· International Journal of Nutrition and lifestyle· Vol 6, pp. 56-61· 0 citations
TL;DR
GenAI works best as a scalable supplement to trained nutritionists rather than as a replacement for them, according to a review of the current peer-reviewed articles.
Abstract
Artificial intelligence is drastically entering every aspect of our lives, including education, training, and communication. There are many models in Generative artificial intelligence (GenAI) such as “large language models (LLMs) including ChatGPT, Gemini, Claude, and Copilot etc. This narrative review focuses on the current peer-reviewed articles (predominantly 2023–2026) on the AI applications, opportunities, challenges and future directions in the field of nutrition education. Most of these applications span three domains: professional training, direct-to-consumer dietary counselling and meal planning (chatbot-delivered advice, personalised diet plans), and public health nutrition communication. Evidence suggests that GenAI can improve nutrition learning's accessibility, scalability, personalisation, and engagement while providing dietetics educators with an affordable substitute for resource-intensive training techniques like standardised patients. However, studies consistently show limitations in accuracy, especially when it comes to calculations of calories and macronutrients, complex or comorbid clinical scenarios, and culturally specific dietary contexts. These limitations are accompanied by concerns about misleading information, algorithmic bias, dependency, data privacy, and the deterioration of critical thinking. Future directions include multimodal food-image analysis, retrieval-augmented generation based on validated nutrition databases, hybrid human-AI counselling models, and formal AI-literacy curriculum for the general public and dietetics students. According to the review, GenAI works best as a scalable supplement to trained nutritionists rather than as a replacement for them.
Generative artificial intelligence (AI) has emerged as a rapidly evolving technology with growing applications in healthcare education. Large language models, such as ChatGPT, Gemini, Claude and Microsoft Copilot, are increasingly being used to support learning, assessment, scientific writing and educational content development. In dental education, these technologies offer opportunities to enhance access to information, facilitate self-directed learning, provide personalized educational support, and improve student engagement.
This narrative review summarizes the current applications of generative AI in dental education and discusses its potential benefits, challenges, and future implications. Current evidence suggests that AI can support theoretical learning, clinical reasoning, assessment, feedback, and research-related activities. However, important concerns remain regarding the accuracy and reliability of AI-generated information, hallucinations, academic integrity, bias, data privacy, and excessive dependence on automated systems.
The successful integration of generative AI into dental curricula requires the development of AI literacy, ethical guidelines, and evidence-based implementation strategies. Although AI technologies have considerable potential to enhance educational processes, they should complement rather than replace educators, critical thinking, clinical reasoning, and hands-on clinical training. Continued research is needed to evaluate the long-term educational impact of AI and to establish best practices for its responsible use in dental education.
Ivet Dzhondrova, Dimitar Kirov· Problems of Dental Medicine· 0 citations
Background Generative artificial intelligence (GenAI), particularly large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4, is rapidly being integrated into sports medicine practice. These tools are increasingly used by health professionals, coaches, and athletes for training prescription, rehabilitation, nutrition, mental health support, injury prevention, and academic writing. However, their adoption has outpaced the development of robust evidence regarding their clinical utility, accuracy, and safety, and no comprehensive synthesis of this emerging field currently exists. Aim This scoping review aimed to map the current evidence on GenAI and LLM applications in sports medicine and athlete health, evaluate their accuracy and hallucination risks across domains, and synthesise reported ethical and governance concerns. Methods The review followed PRISMA-ScR guidelines and Joanna Briggs Institute methodology. Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and CINAHL) were searched for studies published between January 2022 and March 2026. Eligibility criteria were defined using the Population–Concept–Context framework. Two independent reviewers conducted screening, full-text assessment, and data extraction, with strong inter-rater agreement (κ = 0.82). Results Of 1,847 records identified, 32 studies were included. Applications were classified into seven domains: training and exercise prescription (n = 5), nutrition (n = 4), rehabilitation (n = 3), mental health (n = 3), clinical decision support (n = 5), academic writing (n = 6), and ethics/governance (n = 6). LLM accuracy varied substantially: in a single validation study, content validity ratios for sleep recommendations ranged from 0.33 (GPT-3.5) to 0.67 (GPT-4), while only GPT-4 achieved acceptable validity for jet lag guidance (CVR = 0.68). In one bibliometric analysis, AI-generated text in sports medicine journals increased from 2.38% (early 2023) to 6.25% (late 2024). Hallucination risk was rated critical for general-purpose chatbots but substantially reduced in retrieval-augmented systems. Conclusion GenAI shows promise as a supervised decision-support tool in sports medicine, but current evidence does not support unsupervised clinical use. Key challenges include hallucination risks, a lack of sport-specific validation datasets, and insufficient ethical and governance frameworks. Addressing these gaps is essential before widespread integration into athlete health and public health contexts.
Ismail Dergaa, Mohamed Amine Dergaa, Mortadha Razzak et al.· Frontiers in Public Health· 0 citations
This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence.
Haoran Chen, Shenglan Xiao, Tong Wan et al.· Journal of Medical Internet...· 0 citations
Generative artificial intelligence (GenAI), particularly large language models (LLMs), is poised to fundamentally transform medical education. Based on a structured literature search of PubMed, Scopus, Web of Science, and Google Scholar, this review synthesizes current evidence on the applications, capabilities, and limitations of GenAI across the medical training continuum. Advanced LLMs demonstrate a formidable command of medical knowledge, consistently achieving passing scores on standardized licensing examinations, with GPT-4 and domain-specific models like Ortho GPT showing particular proficiency. As versatile teaching tools, these models can generate high-quality assessment materials, provide personalized on-demand tutoring, and power interactive virtual patients for clinical reasoning practice. However, this potential is tempered by significant challenges, including a propensity for “hallucinations,” embedded biases that can perpetuate health inequities, linguistic performance disparities, and a fundamental gap in flexible, adaptive clinical reasoning. Integration also raises critical concerns regarding academic integrity, potential over-reliance leading to deskilling, and data privacy. Responsible adoption requires a structured approach encompassing the development of tiered AI competency frameworks, blended curricular integration, dedicated faculty development, and a rigorous research agenda focused on longitudinal learning outcomes. Ultimately, GenAI should be viewed as a powerful augmentative tool, not a replacement for human educators. Its successful integration will depend on leveraging its strengths to enhance efficiency and scalability while preserving the essential humanistic elements of medical practice through expert oversight and validation.
R. Xie, Bei-En Zhang, Lifeng Xiao· Frontiers in Medicine· 0 citations
Artificial intelligence (AI) is increasingly being applied in healthcare, with growing relevance to clinical nutrition. This narrative review examines current and emerging uses of AI in nutrition care within the Nutrition Care Process framework, with attention to assessment, monitoring and evaluation, diagnosis, intervention, and clinical support tools. Current applications include AI-assisted dietary assessment using image recognition, wearable sensors, analysis of continuous glucose and other physiologic data for early risk detection, and support for malnutrition screening and diagnosis. AI is also being explored for identifying micronutrient deficiencies and complications of nutrient excess, as well as for screening and early intervention in eating disorders. In nutrition intervention, AI has potential to support personalized dietary planning, nutrition support in intensive care settings, behavioral interventions, and precision nutrition approaches such as digital twins. Additional applications include clinical decision support and documentation assistance. However, despite its usefulness, concerns about AI systems exist. Its performance depends on the quality of the data used to train it; it can introduce bias, and it can produce inaccurate or misleading outputs. In addition, overreliance on AI may also reduce clinician attentiveness and contribute to cognitive errors. For these reasons, AI should be regarded as a support tool rather than a replacement for human clinical care. Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.
K. Mauldin, Anthony D. Pham, Sneha Dodaballapur et al.· Nutrients· 0 citations
This study shows that artificial intelligence presents both risks and opportunities in the dietitian profession, and draws attention to the fact that professionals should be equipped in terms of knowledge, ethics and digital competence in this transformation process.
Elif Güner, Kübra Yuca, Emel Öztürk et al.· İstanbul Gelişim Üniversites...· 0 citations