Aug 2026· Nutrients· Vol 18· 0 citations· 48 references
Medicine
TL;DR
Artificial intelligence should be viewed as a complement to multidisciplinary HEN expertise, and the strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation.
Abstract
Home enteral nutrition (HEN) is essential for patients with a functioning gastrointestinal tract who cannot meet nutritional needs orally, yet outpatient management remains complex, resource-intensive, and supported by a limited evidence base. Artificial intelligence (AI) may augment HEN care by extending monitoring, education, risk assessment, documentation support, and operational coordination into the home environment. Consistent with the narrative review format, this article uses a pragmatic, transparent synthesis of influential HEN-specific literature, relevant clinical nutrition evidence, and background knowledge from adjacent fields, including home healthcare, chronic disease management, oncology nutrition, telehealth, and software regulation. Direct evidence in established HEN populations remains scarce; therefore, most AI applications should be considered hypotheses or early implementation opportunities rather than proven standards of care. The strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation. Predictive analytics, smart pumps, and precision enteral prescription tools are promising but require prospective HEN-specific validation, interoperability with electronic health records and home-infusion systems, reimbursement pathways, and governance safeguards. Key barriers include dataset bias, limited external validation, alert fatigue, privacy and regulatory concerns, unclear accountability, digital equity, cost uncertainty, and the risk of dehumanizing care. AI should be viewed as a complement to multidisciplinary HEN expertise. Priorities for the near future include HEN registries, standardized outcomes, prospective validation, pragmatic implementation trials, health-economic evaluation, and transparent oversight that preserves clinician accountability and patient-centered care.
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
The global rise in the older adult population brings complex, interrelated biopsychosocial challenges, including nutritional inadequacies, sarcopenia, social isolation, and sleep disturbances. Dealing with these interconnected issues in institutional care settings is increasingly difficult given the restrictions of traditional approaches. This descriptive review analyzes, from a multidisciplinary perspective, the role of Artificial Intelligence (AI)-based technologies in monitoring nutritional status, predicting clinical risks such as malnutrition and sarcopenia, and enhancing psychosocial well-being in elder care. Relevant literature was identified through searches in PubMed/MEDLINE, Scopus, and Web of Science (2016–2026), focusing on peer-reviewed studies published in English. Current literature indicates that AI-based image-processing systems can accurately monitor dietary intake, while machine learning algorithms can enable earlier risk stratification for sarcopenia and inflammatory trajectories using biomarker data. Furthermore, social robots and non-contact sensors have been shown to reduce loneliness among older adults, improve sleep quality, and indirectly enhance motivation for eating. From a social work perspective, AI is considered an effective tool that increases organizational efficiency in case management and facilitates a shift from crisis-oriented intervention to predictive and preventive care models. The success of this technical transformation depends on establishing an ethical framework that supports privacy, dignity, and the principles of person-centered care. Overall, AI provides evidence-informed decision support for dietitians and social work professionals, helping to develop a holistic care ecosystem that optimizes the well-being of older adults.
İrem Nur Şahin Anılgan, Onur Zeki Anılgan· İstanbul Gelişim Üniversites...· 1 citation
This study aimed to establish a standardized management system for home enteral nutrition (HEN) for patients with postoperative oral cancer and provide scientific guidance for clinical professionals by systematically searching relevant literature and summarizing the best evidence.
Literature retrieval was performed systematically across Chinese and English databases following the “5S” evidence hierarchy model to identify studies addressing home nutritional management in postoperative oral cancer patients. Eligible articles underwent rigorous screening, methodological appraisal, and data synthesis to extract high-quality clinical evidence.
The final review encompassed a total of 15 articles, including 6 evidence-based guidelines, 4 systematic reviews, 1 clinical decision, 2 expert consensuses, and 2 evidence summaries. Through systematic induction and integration, a total of 29 pieces of best evidence were synthesized and categorized into five key domains: establishment of nutrition management team, nutrition screening and assessment procedures, implementation standards for HEN, health education and care strategies, and monitoring and follow-up mechanisms.
The best–evidence–based evidence summarized in this study can provide a reference for the formulation of HEN management plans for postoperative patients with oral cancer.
This study was conducted in accordance with the evidence summary reporting guidelines established by the Fudan University Center for Evidence-based Nursing on 10 July 2025, with the registration number ES20258489.
Aim: To evaluate the effectiveness of artificial intelligence (AI)-based chatbots in virtual nursing care at home, comparing them with traditional care, in terms of therapeutic adherence and improvement of clinical outcomes. Methods: A systematic review was conducted on March 7, 2025, according to the the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The databases consulted were PubMed (Medline) and the Cochrane Library. Studies published since 2020 in Italian or English were included. After the screening process, six studies dealing directly or indirectly with the use of AI in home nursing consultation were included. Results: The analysis shows that chatbots and AI systems improve monitoring, therapeutic adherence, and access to healthcare services. Specifically, AI-based nutritional models showed improved anthropometric and biochemical indicators and a reduction in nutritional risk in patients with chronic kidney disease. Smart home systems have improved the timeliness of clinical event detection and the nurse’s decision-making role; medication support robots have reduced nursing workload and improved therapeutic safety. Critical issues related to training, professional acceptance, and technological infrastructure have emerged, limiting the full integration of AI into home care. Conclusion: This review highlights a growing interest in integrating AI into home nursing care, with promising preliminary results. The study fills a gap in the literature by identifying current trends, benefits, and limitations and highlights the need for further clinical research to validate the effectiveness of AI-based nursing chatbots.
Large language models (LLMs) are rapidly entering clinical workflows, including medical nutrition therapy (MNT) planning, patient education, and documentation support. Their promise lies in speed, personalization, and scalability; however, unsafe outputs can occur due to hallucinations, guideline drift, missing contraindications (e.g., renal/hepatic failure, pregnancy, pediatrics), and overconfident language. This practice-focused review synthesizes current evidence and real-world considerations for using LLMs in clinical nutrition and dietetic practice, with emphasis on patient safety, accountability, and governance. We map high-impact clinical use cases across inpatient and outpatient settings and summarize common failure modes and risk amplifiers (insufficient clinical context, poor prompt hygiene, lack of verification, and inadequate oversight). Current evidence supports broader use in low- to moderate-risk educational, communication, documentation, and population-level applications, whereas individualized therapeutic use in medically complex patients should remain restricted to non-autonomous, clinician-verified workflows. We then propose an implementation framework grounded in human-in-the-loop review, scope restriction, documentation/logging, periodic re-validation, and escalation pathways for red-flag conditions. The manuscript provides a clinic-ready “LLM Safety & Quality Checklist” to support dietitians and clinical teams in verifying outputs against authoritative guidelines, identifying high-risk cases requiring clinician review, ensuring transparent disclosure to patients, and safeguarding privacy. By translating emerging evidence into actionable steps, this paper aims to help dietetic services adopt LLMs responsibly while balancing innovation with patient safety, ethics, and regulatory readiness.
Sedat Arslan· Academia Nutrition and Diete...· 0 citations
Precision nutrition in critical illness should be understood as a longitudinal clinical strategy rather than a fixed prescription for calorie and protein delivery. This structured narrative review used PubMed/MEDLINE as the primary database and supplemented the search through Web of Science Core Collection and Google Scholar. Publications from January 2020 to January 2026 were prioritized, while earlier landmark trials, guidelines, and consensus papers were retained when relevant. Evidence from 73 references, including guidelines, randomized trials, systematic reviews, and relevant observational or mechanistic studies, was synthesized. We propose an operational framework with bedside indicators for early acute illness, stabilization/prolonged ICU care, and post-ICU recovery. During early acute instability, safe initiation and tolerance-based progression are favored over immediate completion of calculated targets. During stabilization and recovery, greater emphasis is placed on correcting persistent deficits, preserving lean mass, and supporting rehabilitation. Feeding intolerance is interpreted as gastrointestinal dysfunction, avoidable interruption, aspiration risk, or persistent inability to sustain enteral nutrition. Post-pyloric feeding and supplemental parenteral nutrition are selective escalation strategies, with safeguards against overfeeding and metabolic complications. After ICU discharge, quantified intake, swallowing function, muscle mass, functional trajectory, and participation in rehabilitation should guide continued support. The framework contextualizes existing recommendations and requires prospective validation.
Pengcheng Tian, Ming Fan, Chaolin Huang· Frontiers in Nutrition· 0 citations