Evidence map›Paper›PMID 40804204›Full record

ReviewCell and tissue research2025

Smarter stem cells: how AI is supercharging iPSC technology.

Hany E Marei

Abstract readReview
In one paragraph

Review in Cell and tissue research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Hany E MareiDepartment of Cytology and Histology, Faculty of Veterinary Medicine, Mansoura University, Mansoura, Egypt. hanymarei@mans.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrated with artificial intelligence (AI), induced pluripotent stem cell (iPSC) technology could enhance disease modeling, cellular biology, regenerative medicine, and pharmaceutical development. AI has enhanced iPSC differentiation, cultural conditions, and speed of disease-specific model development. Furthermore, AI-based massive omics database analysis exposes hidden biological tendencies, enhancing customized treatment. Investigating new AI algorithms will enable one to solve problems, including interpretability and data quality, resulting from AI's interaction with iPSC technology. These advances fundamentally alter stem cell research and therapeutic applications, therefore facilitating the emergence of regenerative medicine and precision healthcare. AI has evolved in biomedical research into a transformational technology unique in great data analysis, predictive modeling, and automation capacity. AI integration increases the development of patient-specific cell types for disease modeling, pharmacological research, and regenerative medicine by substantially improving IPSC-based technologies. Emphasizing changes in disease models, alternative methodologies, and cellular reprogramming, this work examines current advancements in the use of AI in iPSC technology. The argument on significant obstacles and possibilities reveals how AI could alter the objectives of iPSC research and implementation.

Indexed as

Artificial IntelligenceInduced Pluripotent Stem CellsAnimalsCell DifferentiationHumansRegenerative MedicineAICellular reprogrammingDifferentiation protocolsDisease modelingDLDrug discoveryHigh-content imagingInduced pluripotent stem cells (iPSCs)ML(ML)Multi-omicsPersonalized medicinePredictive modelingRegenerative medicineReinforcement learningTranscriptomics

Identifiers

PMID40804204
PMCPMC12727703

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.