ReviewJournal of preventive medicine and public health = Yebang Uihakhoe chi2025
The Next Frontiers in Preventive and Personalized Healthcare: Artificial Intelligent-powered Solutions.
Review in Journal of preventive medicine and public health = Yebang Uihakhoe chi, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Physician versus patient use of AI for diabetes prevention: public perceptions and comfort levels.Frontiers in digital health · 2026Article
- AI-enabled cardiovascular devices: a lifecycle playbook for evidence, change control, and post-market assurance.Frontiers in digital health · 2026Review
- Can AI developers avoid bias in public health applications?Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-enabled technologies have the potential to significantly increase diagnostic accuracy, optimize treatment strategies, and improve patient outcomes. They are revolutionizing the field of preventive and personalized medicine by providing data-driven insights. AI is capable of analyzing large and complex datasets such as genomic, environmental, and lifestyle information much faster and more conveniently than traditional methods. Advanced algorithmic architectures in AI can predict disease risks, identify biomarkers, and tailor interventions to individual needs. The enabling role of AI in real-time monitoring, predictive analysis, and drug discovery demonstrates its transformative potential in healthcare. The role of AI in multi-omics integration, wearable technologies, and precision therapies promises to redefine global healthcare paradigms, making personalized medicine more accessible and effective. However, ethical concerns that need to be addressed to ensure fair and transparent implementation include data privacy, algorithmic bias, and regulatory gaps. This article examines the integration of AI technologies with personalized healthcare. The study also highlights the need for interdisciplinary collaboration to maximize the benefits of AI in preventive and personalized healthcare and overcome barriers.
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.