ReviewAIMS public health2025
Health effects of intermittent fasting and the role of artificial intelligence technologies in optimizing its clinical translation.
Review in AIMS public health, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a non-pharmacological approach, Intermittent Fasting (IF) exhibits the capacity to boost health and counteract chronic diseases by regulating the metabolism, strengthening the cellular resistance to stress, and reshaping the immune microenvironment. The rapid progress of Artificial Intelligence (AI) technologies has greatly advanced our comprehension of IF's diverse health benefits. This review outlines AI's role in enhancing the exploration of IF's function in governing systemic health, clarifies the association between IF and health outcomes, and specifies AI's function in analyzing IF's impacts, which cover metabolic processes, cellular stress responses, disease prevention, and the development of personalized dietary strategies. By leveraging AI to integrate various omics datasets, the mechanisms through which IF prevents chronic diseases can be uncovered. This review discusses the challenges that AI faces in researching diet-related health mechanisms and presents an outlook on future developments. AI offers innovative methods to investigate IF's effects on chronic disease prevention, which could lay the foundation for more efficient strategies to support healthier and longer lifespans.
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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 Socratic graph.