ReviewCurrent stem cell research & therapy2026
Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers.
Review in Current stem cell research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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, 1 synthesis or guideline pooled it.
- Contemporary management of treatment-related dermatologic toxicities in gynecologic cancers: a systematic review based on evidence from 2021 to 2025.Frontiers in medicine · 2026Pooled it
- RNAi-mediated p38δ silencing mitigates anthracycline cardiotoxicity in female mice.American journal of physiology. Heart and circulatory physiology · 2026Article
- Preclinical models of stem cell-mediated analgesia and tissue repair: mechanisms, challenges, and future directions.Frontiers in cellular neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of artificial intelligence (AI) in stem cell and gene therapy offers significant advancements in the treatment of gynecological cancers, including breast, ovarian, and cervical cancers. This review explores how machine learning (ML) enhances both diagnostic and therapeutic strategies in regenerative medicine. AI integration allows for more accurate disease progression predictions, identification of therapeutic targets, and optimization of personalized treatment plans. Additionally, AI improves the efficacy and safety of stem cell and gene therapy approaches by facilitating the identification of biomarkers and genetic variations, enabling tailored therapies for individual patients. The use of AI-supported analytics in combined treatment strategies presents new avenues for effective cancer management. Furthermore, AI-driven regenerative medicine optimizes stem cell functions, refines treatment protocols, and contributes to the identification of less frequent biomarkers, improving prognostic algorithms and therapy outcomes. As ML targets specific molecular changes in cancer cells, they enhance the precision of gene silencing and anti-aging interventions, offering new possibilities for combined therapies. These innovations position AI as a transformative tool in the development of personalized and effective treatments for women's cancers, with future studies likely to expand the scope and impact of AI-driven strategies.
Indexed as
Identifiers
40671229What 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.