ArticlePlastic and reconstructive surgery. Global open2026
Artificial Intelligence-predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward.
Article in Plastic and reconstructive surgery. Global open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is increasingly being applied across medical disciplines to enhance clinical decision-making. However, its use in plastic and reconstructive surgery remains early in development. This review consolidates existing literature to evaluate how AI is specifically being implemented to predict breast reconstruction (BR) outcomes, identify current limitations, and outline priorities for clinical translation. Methods: A literature search was conducted using PubMed and Scopus databases to identify studies published between 2015 and 2025 that applied AI or machine learning to predict outcomes after BR. Eligible studies were categorized into surgical, aesthetic, and patient-reported outcomes domains and synthesized using a conceptual framework of "progress, pitfalls, and path forward." Results: Of 442 studies identified, 25 met the inclusion criteria. Ten studies demonstrated progress in applying AI to predict complications, donor-site morbidity, tissue-expander loss, and patient-reported satisfaction. Three studies highlighted key pitfalls, including limited generalizability due to small, single-center datasets and technical barriers to clinical implementation. Twelve studies focused on future directions, including the prediction of postoperative complications, perfusion-related events, tissue expansion dynamics, and automated aesthetic assessment. Collectively, these findings suggest that AI can model complex, nonlinear relationships among patient, procedural, and aesthetic variables in BR. Conclusions: AI applications in BR are evolving from proof-of-concept toward clinical relevance. Broader validation using multicenter datasets and standardized methodologies will be essential. Collaboration between surgeons and AI engineers will be critical to develop robust, generalizable tools that enhance preoperative planning, intraoperative decision-making, and postoperative outcomes.
Identifiers
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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.