ReviewFrontiers in public health2026
Research integrity and data ethics in AI-driven integrated healthcare: a critical appraisal.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation.Bioengineering (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The convergence of artificial intelligence (AI) and healthcare is reshaping clinical practice, yet this transformation raises pressing questions about scientific rigor and ethical responsibility. This review provides a critical appraisal of research integrity and data ethics considerations specific to AI implementation in integrated healthcare settings. We analyzed peer-reviewed literature from 2019 to 2025, focusing on algorithmic transparency, model validation and reproducibility, bias detection, privacy protection, informed consent paradigms, and governance frameworks. Our analysis reveals a fundamental tension: the data-intensive nature of AI development often conflicts with established principles of patient autonomy and data protection. The opacity of deep learning models challenges conventional standards of scientific transparency, while datasets reflecting historical healthcare disparities risk encoding and amplifying bias. We propose an integrated governance model that aligns technical validation with ethical oversight, emphasizing the need for prospective clinical trials, diverse stakeholder engagement, and adaptive regulatory approaches. This review offers practical guidance for researchers, clinicians, and policymakers navigating the complex intersection of AI innovation and healthcare ethics.
Indexed as
Identifiers
What OpenQuestion holds
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.