ReviewStem cell reviews and reports2022
Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning.
Review in Stem cell reviews and reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Review
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- A Xeno-Free Protocol for Rapid Differentiation of Human iPSC-Derived Microglia from the KOLF2.1J Reference Line.Bioengineering (Basel, Switzerland) · 2025Article
- Smarter stem cells: how AI is supercharging iPSC technology.Cell and tissue research · 2025Review
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- Innovative strategies for bone organoid: Synergistic application and exploration of advanced technologies.Journal of orthopaedic translation · 2025Review
- Applications of artificial intelligence in stem cell therapy.World journal of stem cells · 2025Review
- A xeno-free protocol for rapid differentiation of human iPSC-derived microglia from the KOLF2.1J reference line.bioRxiv : the preprint server for biology · 2025Article
- AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.Biotechnology journal · 2025Review
- Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI.Computational and structural biotechnology journal · 2025Article
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Review
- CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.Bioengineering (Basel, Switzerland) · 2024Article
- A literature review: machine learning-based stem cell investigation.Annals of translational medicine · 2024Review
- Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications.Signal transduction and targeted therapy · 2024Review
- Article
- Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development.Scientific reports · 2023Article
- Unlocking the potential of bioanalytical data through machine learning.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2023Article
- Artificial Intelligence Supports Automated Characterization of Differentiated Human Pluripotent Stem Cells.Stem cells (Dayton, Ohio) · 2023Article
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
The advent of induced pluripotent stem cell (iPSC) technology, which allows to transform one cell type into another, holds the promise to produce therapeutic cells and organs on demand. Realization of this objective is contingent on the ability to demonstrate quality and safety of the cellular product for its intended use. Bottlenecks and backlogs to the clinical use of iPSCs have been fully outlined and a need has emerged for safer and standardized protocols to trigger cell reprogramming and functional differentiation. Amidst great challenges, in particular associated with lengthy culture time and laborious cell characterization, a demand for faster and more accurate methods for the validation of cell identity and function at different stages of the iPSC manufacturing process has risen. Artificial intelligence-based methods are proving helpful for these complex tasks and might revolutionize the way iPSCs are managed to create surrogate cells and organs. Here, we briefly review recent progress in artificial intelligence approaches for evaluation of iPSCs and their derivatives in experimental studies.
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.