ReviewFrontiers in oral health2026
Role of artificial intelligence in determining nutritional risk factors among post-periodontal surgical patients. A scoping review.
Review in Frontiers in oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: This scoping review examines recent peer-reviewed literature (2019-2025) on the role of artificial intelligence (AI) in managing nutrition care for post-periodontal surgical patients, and identifies key risk factors influencing nutritional outcomes after periodontal surgery. AI modalities considered include machine learning, expert systems, clinical decision support, and predictive analytics. Methodology: A systematic search of databases (e.g., PubMed, Scopus) identified studies on AI applications in periodontology, nutrition, or wound healing. The inclusion criteria were English-language, peer-reviewed publications from 2019 onwards that focused on AI in periodontal care or nutritional management, and studies addressing risk factors (such as age, comorbidities, dietary compliance, oral function, socioeconomic status, etc.) that affect post-surgical nutrition or healing. Data were charted on study characteristics, AI type, nutritional outcomes, and reported risk factors. 28 publications were included (10 original studies, eight reviews, five clinical reports, five conceptual papers). AI has been used in periodontal care for diagnostics, prognostics, and decision support. Results: Machine learning models can predict healing and nutritional risks by analyzing patient data, with key risk factors including age, comorbidities such as diabetes, poor nutrition, low dietary compliance, oral function, and socioeconomic status. Older, chewing-impaired patients have lower nutrient intake and a higher risk of malnutrition. Poor pre-surgery nutrition delays healing. AI models forecast outcomes, identifying baseline pocket depth and antibiotic use as strong predictors. Emerging AI tools in periodontology can enhance nutrition management through early risk detection and personalized diets. Conclusion: Factors like age, health, oral function, and socioeconomic status affect recovery. Using AI risk assessments with nutritional plans may improve healing. More research is needed to realize AI's full potential. While direct studies are limited, emerging evidence indicates strong potential for personalized, AI-supported nutritional care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.