ReviewNature reviews. Gastroenterology & hepatology2026
Synthetic data generation: challenges and perspectives for gastrointestinal medicine.
Review in Nature reviews. Gastroenterology & hepatology, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of artificial intelligence, machines are demonstrating an unprecedented capacity to learn from massive amounts of real-world data to perform human-like cognitive processes, enabling them to recognize environments, objects, and conditions and make critical decisions more accurately than ever. In the medical field, the potential to generate realistic, privacy-preserving, unbiased synthetic data can be the key to unlocking the potential of artificial intelligence in medicine and overcoming the current barriers such as data privacy concerns and high data curation costs. Advanced data-driven solutions could lead towards more robust clinical decision support systems and enhanced clinical training. This Perspective critically examines current and emerging advances in synthetic data generation, and highlights its anticipated transformational effect for early and efficient prevention, diagnosis and treatment of gastrointestinal diseases. Research challenges and directions are identified for leveraging the benefits of synthetic data as well as translating and adopting them in clinical workflows.
Indexed as
Identifiers
42310470What Socratic 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 Socratic graph.