ArticleBioinformation2026
The Artificial Intelligence (AI) paradox.
Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The adoption of artificial intelligence (AI), from routine in every day usage to specialized interventions in dentistry and medicine, from personal needs to sophisticated business applications, has skyrocketed in high-income as well as in developing nations in the last decade. Inequalities remain however, and while AI utilization is practically worldwide, the divide between the northern and southern hemispheres is widening in terms of AI as the foundational infrastructure of modernity, and specifically of contemporary digital life. However, and to some extent paradoxically, the more AI is studied, developed and improved, the more we uncover its weaknesses, limitations, and, as some have argued, its inherent individual and societal dangers. AI generates information based on algorithms that are derived from factual data, which may, or may not have been verified by evidence-based science: information that carries the risk of being biased or fallacious at best, or old and invalidated by new research at worst. Critics of AI also observe its limits, such as in the context of basic human emotional and psychological-social skills. To be clear and as discussed in this writing, the more AI utilization grows and becomes more widespread, the more evident its limitations in reliability and validity become evident.
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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.