ReviewBiomaterials translational2024
Revolutionising oral organoids with artificial intelligence.
Review in Biomaterials translational, 2024. 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.
- Bone Organoids as Advanced Models for Osteoporosis: Development, Application, and Future Prospects.International journal of molecular sciences · 2026Review
- Recapitulating lung cancer metastasisOncology reports · 2026Review
- Organoids: the future of disease modelling and therapeutics.Biomaterials translational · 2024Article
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 convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional structures derived from human tissues - offer invaluable insights into the complex biology of diseases, allowing researchers to effectively study disease mechanisms and test therapeutic interventions in environments that closely mimic in vivo conditions. In this review, we first present the historical development of organoids and delve into the current types of oral organoids, focusing on their use in disease models, regeneration and microbiome intervention. We then compare single-source and multi-lineage oral organoids and assess the latest progress in bioprinted, vascularised and neural-integrated organoids. In the next part of the review, we highlight significant advancements in AI, emphasising how AI algorithms may potentially promote organoid development for early disease detection and diagnosis, personalised treatment, disease prediction and drug screening. However, our main finding is the identification of remaining challenges, such as data integration and the critical need for rigorous validation of AI algorithms to ensure their clinical reliability. Our main viewpoint is that current AI-enabled oral organoids are still limited in applications but, as we look to the future, we offer insights into the potential transformation of AI-integrated oral organoids in oral disease diagnosis, oral microbial interactions and drug discoveries. By synthesising these components, this review aims to provide a comprehensive perspective on the current state and future implications of AI-enabled oral organoids, emphasising their role in advancing oral healthcare and improving patient outcomes.
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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.