ReviewJournal of clinical and experimental hepatology
Challenges and Future Perspectives for Artificial Intelligence in Hepatology: Breaking Barriers for Better Care.
Review in Journal of clinical and experimental hepatology. 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.
- Mapping artificial intelligence adoption in hepatology practice and research: challenges and opportunities in MENA region.Frontiers in medicine · 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) presents a compelling opportunity to revolutionize the practice of hepatology through a myriad of novel approaches ranging from predictive modeling to patient-specific clinical decision support systems. While AI will undoubtedly transform clinical practice in the coming years, there remains an evolving set of challenges to the implementation of AI. In this review article, we address technical and stakeholder barriers to the adoption of AI and potential repercussions if they remain unaddressed. We highlight strategies to mitigate these potential pitfalls and the need for prospective research to confirm model validity. Lastly, we look to the future of what AI in clinical practice will mean for patients and clinicians.
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.