Evidence map›Paper›PMID 42688144›Full record

ArticleFrontiers in public health2026

The algorithmic epistemic inversion in gastroenterology: how social media and psychological vulnerability fuel secondary risks in IBD and IBS management.

Li An, Mingchun Luan, Yongping Yang, Siping Wang

Abstract read
In one paragraph

Article in Frontiers in public 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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Li AnYantaishan Hospital, Yantai, China.
Mingchun LuanYantaishan Hospital, Yantai, China.
Yongping YangYantaishan Hospital, Yantai, China.
Siping WangYantaishan Hospital, Yantai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The unchecked proliferation of medical misinformation on short-video platforms has crystallized into a critical digital public health crisis. For patients managing chronic gastrointestinal diseases such as IBD and IBS, recommendation algorithms routinely override evidence-based medicine (EBM), engendering a phenomenon we term "algorithmic epistemic inversion." This inversion amplifies unproven "miracle cures" and fear-based narratives, driving population-level secondary risks, ranging from indiscriminate probiotic abuse and extreme elimination diets, malnutrition, and orthorexia nervosa. Although IBD and IBS are primarily gastrointestinal diseases, these algorithmically induced secondary risks impose severe, systemic damage on the central nervous system (CNS) and broader neurocognitive functions. To conceptualize this crisis, we introduce the novel framework of the Brain-Gut-Algorithm Axis, which delineates how algorithm-amplified anxiety acts as a social contagion exacerbating somatic symptoms through established gut-brain pathways. To counter this infodemic, we advocate for a public health governance framework grounded in AI-driven algorithmic pharmacovigilance. We urge policymakers and clinicians to recognize algorithm hegemony as a critical digital determinant of health by advancing health equity and integrating psychogastroenterology into routine clinical practice. This Perspective provides specific regulatory and clinical strategies that are crucial to protecting vulnerable populations and strengthening digital public health governance.

Indexed as

AlgorithmsGastroenterologyInflammatory Bowel DiseasesIrritable Bowel SyndromeSocial MediaEvidence-Based MedicineHumansalgorithmic epistemic inversionBrain-Gut-Algorithm AxisIBDIBSmedical misinformationpsychogastroenterology

Identifiers

PMID42688144
PMCPMC13534024

What OpenQuestion holds

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Registered trials

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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.