Artificial Intelligence in Breast Reconstruction: A Narrative Review
Rugină AI., Ungureanu A., Giuglea C., Marinescu SA.
Clinical Trial, published in Medicina (Kaunas) (2025) — summary generated from the PubMed abstract.
Several human studies show positive signals, while research methods and sample sizes continue to develop.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Clinical Trial
- Journal
- Medicina (Kaunas) (2025)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 40142251
- PMCID
- PMC11944005
- DOI
- 10.3390/medicina61030440
- Citations
- 8
Abstract (original English)
Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer patients. Since its inception in the 1950s, artificial intelligence (AI) has gradually entered the medical field, promising to transform surgical planning, intraoperative guidance, postoperative care, and medical research. This article examines AI applications in breast reconstruction, supported by recent studies. AI shows promise in enhancing imaging for tumor detection and surgical planning, improving microsurgical precision, predicting complications such as flap failure, and optimizing postoperative monitoring. However, challenges remain, including data quality, safety, algorithm transparency, and clinical integration. Despite these shortcomings, AI has the potential to revolutionize breast reconstruction by improving preoperative planning, surgical precision, operative efficiency, and patient outcomes. This review provides a foundation for further research as AI continues to evolve and clinical trials expand its applications, offering greater benefits to patients and healthcare providers.
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Several human studies show positive signals, while research methods and sample sizes continue to develop.
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