ReviewFrontiers in big data2025
Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape.
Review in Frontiers in big data, 2025. 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
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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.
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.
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prevalence of immune diseases is rising, imposing burdens on patients, healthcare providers, and society. Addressing the future impact of immune diseases requires "big data" on global distribution/prevalence, patient demographics, risk factors, biomarkers, and prognosis to inform prevention, diagnosis, and treatment strategies. Big data offer promise by integrating diverse real-world data sources with artificial intelligence (AI) and big data analytics (BDA), yet cautious implementation is vital due to the potential to perpetuate and exacerbate biases. In this review, we outline some of the key challenges associated with achieving health equity through the use of big data, AI, and BDA in immune diseases and present potential solutions. For example, political/institutional will and stakeholder engagement are essential, requiring evidence of return on investment, a clear definition of success (including key metrics), and improved communication of unmet needs, disparities in treatments and outcomes, and the benefits of AI and BDA in achieving health equity. Broad representation and engagement are required to foster trust and inclusivity, involving patients and community organizations in study design, data collection, and decision-making processes. Enhancing technical capabilities and accountability with AI and BDA are also crucial to address data quality and diversity issues, ensuring datasets are of sufficient quality and representative of minoritized populations. Lastly, mitigating biases in AI and BDA is imperative, necessitating robust and iterative fairness assessments, continuous evaluation, and strong governance. Collaborative efforts to overcome these challenges are needed to leverage AI and BDA effectively, including an infrastructure for sharing harmonized big data, to advance health equity in immune diseases through transparent, fair, and impactful data-driven solutions.
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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.