AI evidence extraction

Transparency gap in AI-assisted medical writing and its implications for research integrity

Vita Widyasari · Jurnal kedokteran dan kesehatan Indonesia · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.20885/jkki.vol17.iss1.art1

Methodology & findings

Study design

The journal issue contains multiple distinct methodologies including prognostic scoring model development, observational studies on cosmetic usage, experimental studies in animal models (pregnant Balb/c mice), genetic association studies, therapeutic efficacy studies, epidemiological surveys of disease occurrence, clinical case series, and a systematic review of dermatological interventions..

Sample

unknown

Main result

The April 2026 issue presents multiple empirical studies demonstrating diverse clinical and experimental findings. Key results include: a prognostic scoring model for traumatic intracerebral hemorrhage that "can help clinicians estimate outcomes and guide early management decisions"; an association between facial cosmetic usage and sensitive skin incidence among female medical students; experimental evidence in pregnant mice suggesting "potential adverse effects of mobile phone radiation on cerebellar Purkinje cells"; identification of MC4R genotype association with body fat percentage as an obesity indicator; and exosome therapy results demonstrating "antiinflammatory effects, evidenced by reduced expression of IL-1β and TNF-α, alongside anatomical improvement in third-degree burn models".

Reports effect sizes.

Research paradigm

positivist

Author conclusions

The authors conclude that "progress in healthcare is not solely dependent on cutting-edge technology, but also on the effective integration of clinical evidence, environmental risk awareness, and emerging biological therapies. Strengthening predictive capabilities, addressing modern exposures, and adapting innovations within local healthcare contexts remain essential strategies for improving health outcomes."

Risk of bias

Not explicitly stated in the document. Potential risks inherent to the mixture of study designs (animal models may not generalize to humans; case reports are prone to selection bias; observational studies lack randomization) but not formally discussed.

Open questions raised

The editorial identifies gaps including the need for improved awareness of safe dermatological practices regarding facial cosmetics, further investigation of technological exposure effects on neurodevelopment, personalized approaches to obesity prevention and management based on genetic factors, and heightened vigilance in diagnosing kidney disorders in pediatric populations.
The document identifies gaps in: (1) safe dermatological practices awareness among medical students, (2) understanding of technological exposure effects on neurodevelopment, (3) personalized approaches to obesity prevention based on genetic factors, (4) diagnostic protocols for pediatric kidney disorders, and (5) preventive strategies for disease occurrence in large-scale population mobility contexts.
The review identifies several areas requiring further investigation: improved awareness of safe dermatological practices regarding facial cosmetics; deeper understanding of technological exposure effects on neurodevelopment; need for personalized approaches in obesity prevention and management; importance of preventive and promotive strategies for large-scale population mobility; heightened vigilance in pediatric kidney disorder diagnosis; and the necessity of multidisciplinary approaches in handling complex medical scenarios.

Extracted from: pdfAgreement 78%

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