Artificial Intelligence–predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward

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.

Takeaways

Question: How is artificial intelligence (AI) being used to predict breast reconstruction outcomes, and where is its clinical translation limited?

Findings: Evidence across 25 studies shows that AI can predict complications, donor-site morbidity, tissue-expander loss, aesthetic results, and patient-reported outcomes in reconstructive breast surgery. Barriers to AI model development include small, single-center datasets, rare event frequencies, and technical challenges.

Meaning: AI’s capability to predict surgical, aesthetic, and patient-reported outcomes in reconstructive breast surgery is promising, but before this technology can be clinically implemented, larger, multicenter datasets and close collaboration with AI engineers are required to strengthen model validity, accuracy, and generalizability.

INTRODUCTION

Once considered to be science fiction, artificial intelligence (AI) and machine learning (ML), a subset of AI, have now become parts of present-day reality. Simply put, AI uses algorithms to perform tasks that typically require human cognition, such as learning, problem-solving, pattern recognition, and decision-making. Its strength lies in its ability to rapidly interpret patterns from expansive datasets, becoming more precise as additional data become available, and autonomously improving through experience.¹ In this review, the terms AI and ML are used interchangeably to reflect their overlapping application in the included literature. This technology has revolutionized the way humans communicate, make decisions, and innovate, but its trajectory for influencing society remains unpredictable.

Across medical specialties, AI is being leveraged for image interpretation, pathology interpretation, dermatologic lesion characterization, electrocardiogram interpretation, endoscopy lesion identification, and even facial analysis.¹ Typically, AI performance is evaluated against physician assessments using a plot of true-positive versus false-positive rates, known as a receiver operating characteristic, where the area under the curve (AUC) quantifies diagnostic accuracy and predictive capacity.² AUC values range from 0.5 (no better than random guessing) to 1.0 (perfect classification), with values above 0.80 considered to be clinically useful and values below 0.80 considered to be of limited clinical utility.³

Although AI’s predictive potential has become well recognized in medicine, its integration into plastic and reconstructive surgery (PRS) remains relatively nascent. Within PRS, reconstructive breast surgery requires a unique harmony between technical precision, aesthetic perception, and patient-centered care. As AI continues to gain traction in medicine, understanding its current ability and potential to optimize breast reconstruction (BR) outcomes is essential.

Between 2012 and 2021, breast cancer rates increased by 1% annually, with a steeper increase of 1.4% annually among women younger than 50 years of age.⁴ In 2024, there were 162,579 reconstructive breast procedures, a 3% increase from 2023.⁵ Despite the increasing incidence of breast cancer, BR, and advancements in surgical techniques, rates of surgical complications after postmastectomy BR are reported to be around 8.4%–10%. These range from minor complications (eg, superficial wound infections, wound dehiscence) to major complications (eg, flap failure, thrombosis, hematoma, necrosis) that require urgent return to the operating room.^6–8

Although existing reviews provide a scoping overview of how AI is being implemented in reconstructive breast surgery, we provide a critical review solely focusing on AI’s ability to predict outcomes of BR. We approach the review through a conceptual framework of progress, pitfalls, and path forward to highlight the current predictive capabilities of AI (progress), limitations such as lack of generalizability and external validation (pitfalls), and future directions for integrating AI into real clinical decision-making and outcome prediction (path forward).

METHODS

Review Approach

Given the heterogeneity of study designs, outcome definitions, and performance metrics, quantitative synthesis and meta-analysis were not feasible. A narrative approach was therefore used to critically evaluate and contextualize existing evidence. Reporting of the search and screening process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines.⁹ Because this was not a formal systematic review, however, a formal risk-of-bias assessment was not performed.

The review was guided by 3 parts of the main question:

1. Progress: How is AI being developed to predict BR outcomes?

2. Pitfalls: What limitations exist in current AI-based outcome prediction?

3. Path forward: Where could AI be implemented in the future to predict BR outcomes?

Search Strategy and Study Identification

A literature search was conducted in September 2025 using PubMed and Scopus to identify articles published between 2015 and 2025 evaluating the application of AI or ML in BR. These databases were intentionally selected to capture both clinically focused surgical literature and AI-related research, providing broad coverage of this emerging field. Search terms combined Medical Subject Headings (MeSH) and keywords: (“artificial intelligence” OR “machine learning”) AND “breast reconstruction.”

Search results were deduplicated in Zotero 2024 (Corporation of Digital Scholarship, Vienna, VA). Titles and abstracts were screened by the lead author (R.S.M.) for relevance, and discrepancies were resolved by the senior author (J.E.J.) as needed.

Inclusion Criteria

Included articles were primary studies that applied AI or ML to predict, assess, or model various outcome domains after BR: surgical, aesthetic, and patient-reported (Table 1). Perioperative AI studies were included if they directly connected preoperative variables (eg, implant sizing, flap selection, perforator mapping) to postoperative outcomes.

Table 1.

Outcomes and Variables

Outcome Domain	Examples
Surgical	Skin-flap necrosis, capsular contracture, infection, implant loss, donor-site morbidity
Aesthetic	Symmetry, contour, projection, 3D measurements
Patient-reported	BREAST-Q scores, satisfaction, quality of life

Exclusion Criteria

Excluded articles focused only on breast cancer imaging, diagnosis, or treatment or general imaging; only included aesthetic breast surgery (augmentation, mastopexy, or reduction); studied large language model–generated responses only (eg, assessing AI chatbots for BR patient education); were unrelated to PRS, did not use AI, were non-English, abstract-only, or classified as derivative articles (eg, systematic, scoping, or narrative reviews, editorials, letters, comments, or viewpoints); or appeared solely in conference supplements. Articles that solely used AI in preoperative planning (eg, implant selection, surgical approach planning, and visualization tools) were also excluded. Although preoperative decision-making can influence surgical outcomes, studies that did not focus on using AI or ML to assess downstream postoperative outcomes were considered outside of the scope of this article and were excluded. Studies meeting inclusion criteria were reviewed in full.

Data Extraction

Extracted data were organized in a Microsoft Excel 2016 (Microsoft Corp., Redmond, WA) spreadsheet, recording the following:

• Study characteristics (author, year, study design, type of reconstruction);

• AI/ML methodology (algorithm used, purpose, input data);

• Outcome domain (surgical, aesthetic, or patient-reported);

• Key performance measures (eg, AUC, accuracy, correlation coefficients, statistical analysis);

• Summary of results;

• Reported limitations (eg, single-center, nongeneralizable findings).

Synthesis Approach

Inductive synthesis was used to compile common themes across studies. Results were organized according to the 3 outcome domains (surgical, aesthetic, patient-reported) and interpreted within a conceptual framework of progress, pitfalls, and path forward to address the main research question.

RESULTS

Initially, 442 total articles were identified across PubMed and Scopus (Fig. 1). After title/abstract screening, 344 articles were excluded. Of 46 articles reviewed in full, 21 articles were excluded: focused solely on aesthetic breast surgery (n = 9); preoperative planning only (n = 2); educational evaluation of large language models (n = 6); not specific to BR (n = 1); and secondary articles (n = 3). In total, 25 articles were included in the synthesis: 19 articles focused on surgical outcomes, 3 on aesthetic outcomes, and 3 on patient-reported outcomes (PROs). Ten were classified as progress, 3 as pitfalls, and 12 as path forward. (See figure, Supplemental Digital Content 1, which categorizes all 12 studies by outcomes, AI applications, and limitations, https://links.lww.com/PRSGO/F183.)

Fig. 1.

PRISMA diagram showing search strategy methodology. BC, breast cancer; dx, diagnosis; tx, treatment.

Across included studies, a range of AI/ML models were used, including random forest, extreme gradient boosting, and neural networks. Given the heterogeneity, direct comparison of model performance and associated biases across studies was not feasible within the scope of this narrative review.

PROGRESS

Fifteen studies demonstrated advancements in AI for predicting BR outcomes^10–24 (Table 2). The most common AI performance evaluation metrics included AUC and accuracy.

Table 2.

Different Outcomes

Outcome	Variable of Interest	Studies
Surgical	Breast complications	Naoum et al,10 Chen et al11
Surgical	Donor-site complications	Myung et al,12 Huang et al13
Surgical	TE/implant loss	Hassan et al,14 Chen et al15
Surgical	NAC or skin-flap necrosis	Braun et al,16 Hassan et al,18
Surgical	Postoperative sequelae	Chen et al20
Patient-reported	BREAST-Q satisfaction scores	Pfob et al,21 Xu et al22
Aesthetic	Breast symmetry evaluation	Kenig et al24

Progress: Surgical Outcomes

Breast Complications

Two retrospective studies suggested that AI can predict postoperative complication risk after autologous or implant-based BRs with moderate discriminatory performance.^10,11 In 2021, Naoum et al¹⁰ conducted a study of 1617 patients and trained an ML model to predict the risk of infection, skin necrosis, capsular contracture, and implant or reconstruction failure. Performance across complications showed moderate agreement between observed and predicted outcomes (AUC 0.68–0.75).¹⁰ Similarly, a study in 2023 by Chen et al¹¹ developed an AI model to predict capsular contracture after 2-stage implant-based reconstruction, achieving AUCs of 0.74–0.79 and accuracies of 71%–83%.

Donor-site Morbidity

Two retrospective studies suggested that ML can not only predict donor-site morbidity but also provide data-driven clinical thresholds to aid in preoperative risk stratification for autologous reconstruction.^12,13 In 2021, Myung et al¹² trained an ML model with 568 patients undergoing transverse rectus abdominis muscle or deep inferior epigastric artery perforator (DIEP) flap to predict donor-site complications (ie, abdominal bulge, hernia, or weakness). The best model achieved an AUC of 0.82 and an accuracy of 81%. Key predictors included fascial defect size of 37.5 cm² or greater, diabetes, adjuvant chemotherapy, and non–muscle-sparing techniques. Patients meeting these criteria had a 26% probability of donor-site complications compared with 1.7% in the low-risk group.¹²

A 2024 study by Huang et al¹³ used AI to analyze 258 DIEP flap reconstructions and identify clinical thresholds associated with donor-site morbidity. The ML models identified optimal cutoff values for increased complication risk: age greater than 53 years (AUC = 0.62), body mass index greater than 26 kg/m² (AUC = 0.67), and operative time greater than 610 minutes (AUC = 0.59). The best overall model achieved an AUC of 0.69 and an accuracy of 82%.¹³

Tissue Expander/Implant Loss

Two retrospective studies suggested that AI could predict complication risk associated with tissue expander (TE) or implant-based reconstruction, potentially supporting preoperative risk assessment.^14,15 A 2023 study by Hassan et al¹⁴ tested 9 ML algorithms trained with 694 immediate TE BRs to predict periprosthetic infection and explantation. Model accuracy ranged from 67% to 83% for periprosthetic infection and 64% to 84% for explantation, with AUCs of 0.73 and 0.78, respectively. The models also identified 9 significant predictors for infection and 12 for explantation.¹⁴

A larger 2025 study of 6513 immediate TE reconstructions identified older age, obesity, smoking, radiation, chemotherapy, and plane placement (partial submuscular or prepectoral) as key predictors of complications. Logistic regression achieved AUCs of 0.71 (TE loss), 0.65 (infection), and 0.69 (seroma), whereas ML models demonstrated comparable or improved predictive performance with AUCs of 0.71, 0.73, and 0.72, respectively.¹⁵

NAC or Skin-flap Necrosis

Three studies found that AI algorithms could identify patients at risk of nipple–areolar complex (NAC) or mastectomy skin-flap necrosis.^16–18

In 2023, Braun et al¹⁶ trained an AI algorithm on retrospective data from 181 immediate breast reconstruction patients to predict NAC necrosis, achieving an AUC of 0.99 and 97% accuracy. The model was prospectively validated in an additional 62 patients, maintaining strong discrimination with an AUC of 0.95 and 97% accuracy.¹⁶ Another 2025 study by Meyer et al¹⁷ evaluated 388 implant-based BR patients using a previously validated ML model to predict NAC necrosis, achieving an AUC of 0.70 and 96% accuracy. In a 2023 study, Hassan et al¹⁸ trained 9 ML models to predict mastectomy skin-flap necrosis in 481 immediate breast reconstructions and found that traditional logistic regression performed less well (AUC = 0.60) than the best-performing AI algorithm (AUC = 0.70, 89% accuracy).

Postoperative Sequelae

Two retrospective studies in 2024 by Chen et al^19,20 suggested that ML algorithms could anticipate postoperative events such as the need for postmastectomy radiation therapy (PMRT) and elective or unplanned surgery. The first study of 800 direct-to-implant reconstruction patients produced an ML model that predicted the need for PMRT with as few as 12 preoperative features and an AUC of 0.79.¹⁹ Another 2024 study used ML models trained on 209 two-stage reconstruction patients to predict the likelihood of elective or unplanned revision surgery within 2 years of the second surgery. The top AI model was a neural network achieving an AUC of 0.74 and an accuracy of 62.1%.²⁰

Progress: Patient-Reported Outcomes

Three multicenter retrospective studies supported ML’s ability to predict long-term, patient-reported BREAST-Q outcomes after BR with strong discrimination and generalizability.^21–23

Two studies in 2021 and 2023 used data from more than 1500 patients across 11 sites to create AI algorithms. The first study focused solely on improved or decreased satisfaction with breasts, achieving AUCs of 0.86–0.87 and 0.84–0.85, respectively.²¹ The second study expanded ML models to predict additional BREAST-Q domains: physical well-being (0.81–0.82), sexual well-being (0.74–0.76), and psychosocial well-being (0.66).²²

Similarly, a 2025 study of 2687 patients developed ML models to predict BREAST-Q scores across 5 domains 1 year after reconstruction. The highest-performing models achieved AUCs of 0.97 for abdominal well-being, 0.86 for satisfaction with breasts, 0.79 for sexual well-being, 0.78 for chest physical well-being, and 0.74 for psychosocial well-being. Model performance differences were not statistically significant except for psychosocial and sexual well-being (P < 0.05).²³

Across the studies, preoperative BREAST-Q scores, reconstruction type, radiation timing, and smoking status were key predictors.^21–23

Progress: Aesthetic Outcomes

A 2024 prospective study by Kenig et al²⁴ found that AI is a viable tool for standardized, objective aesthetic evaluation in BR. An AI model was trained on 622 postoperative images to detect breast symmetry after reconstruction. The algorithm identified key anatomical landmarks essential for assessing symmetry (suprasternal notch, NAC, breast boundaries) with 98.5% accuracy, substantial agreement with human observers (Cohen κ = 0.79–0.83), and more than 15 times faster performance than manual assessment (0.9 versus 14.1 s).²⁴

PITFALLS

Three studies highlighted major limitations that currently restrict clinical translation of AI in informing reconstructive surgical outcomes, including flap failure, capsular contracture, and perforator mapping^25–27 (Table 3).

Table 3.

Surgical Outcomes

Outcome	Variable of Interest	Studies
Surgical	Flap failure	Kim et al,28 Ozmen et al,29 Gabay et al30
Surgical	Capsular contracture	Dalli et al31
Surgical	Perforator mapping	Laudo et al32

Pitfalls: Surgical Outcomes

In 2020, O’Neill et al²⁵ developed an ML model with retrospective data from 1012 cases with 1.1% flap loss to predict free-flap failure after microvascular reconstruction. Due to low event frequency and severe data imbalance, the authors used various resampling and oversampling techniques to improve model efficacy. Although the model achieved an AUC of 0.95 in training, its performance declined markedly in testing (AUC = 0.67) due to severe data imbalance and low event frequency. The authors noted that stable event prediction would require datasets exceeding 7000 cases to outweigh the limitations of underpowered samples and ensure model generalizability.²⁵

Similarly, a 2023 study by Bavaro et al²⁶ used multiple ML algorithms to predict capsular contracture after PMRT and BR in 59 patients. The best-performing model achieved an AUC of 0.68 and an accuracy of 68%, constrained by the small sample size and lack of external validation.²⁶

A 2021 prospective feasibility study by Mavioso et al²⁷ evaluated 40 DIEP flap patients with AI-assisted perforator mapping, an outcome-influencing variable critical for flap perfusion and viability. Manual computed tomography angiography mapping was compared with a semiautomatic computer-vision algorithm, with intraoperative findings used as the ground truth. Although manual mapping required 2–3 hours per case, the AI tool identified perforators in approximately 30 minutes with a mean localization error of approximately 3 mm. Accuracy was high for vessels more than 1.5 mm, but performance for smaller vessels less than 1.5 mm declined and still required manual oversight. This single-center pilot study revealed current technical barriers to reliably integrating AI into automatic perforator mapping.²⁷

Collectively, these studies demonstrate that while AI can detect relevant clinical patterns, predictive performance in surgical outcome domains remains limited by small, imbalanced datasets, lack of generalizability, and technical constraints that currently hinder clinical applicability.

PATH FORWARD

Seven studies identified evolving frontiers for the application of AI in reconstructive breast surgery, spanning postoperative complication prediction, intraoperative perfusion analysis, computational modeling of tissue expansion, and automated breast symmetry assessment^28–34 (Table 4).

Table 4.

Outcomes

Outcome	Variable of Interest	Studies
Surgical	Postoperative complications/readmission	Kim et al,28 Ozmen et al,29 Gabay et al30
Surgical	Flap perfusion	Dalli et al31
Surgical	TE skin expansion	Laudo et al32
Aesthetic	Evaluating breast symmetry	Kenig et al,24 Kenig et al33

Path Forward: Surgical Outcomes

Postoperative Complications

Three retrospective studies showed emerging AI applications by analyzing large National Surgical Quality Improvement Program (NSQIP) BR cohorts to develop ML models integrating patient heterogeneity and operative variables to predict the risk of postoperative complications.^28–30

Kim et al²⁸ conducted a study of 14,272 BRs in 2024 and applied unsupervised ML (k-means clustering) to identify 7 data-driven patient subgroups that differed by age, comorbidity burden, reconstruction type, and complication rates (ranging from 14% to 36%). Some clusters revealed counterintuitive results; for instance, 1 subgroup with higher body mass index, age, and comorbidities, but shorter operative times, had complication rates lower than the cohort average. Although not yet predictive at the individual level, this approach illustrated AI’s capacity to uncover nonlinear relationships and inform future preoperative risk stratification.²⁸

A 2025 study by Ozmen et al²⁹ of 13,312 DIEP flap procedures stacked 5 ML algorithms to predict 30-day readmission, achieving 88% accuracy and an AUC of 0.89, outperforming a single ML model approach (AUC = 0.62). Although the stacked model capabilities seem promising as a potential screening tool, its precision was 28%, reflecting future work required for ML to predict rare events clinically.²⁹

A 2025 study by Gabay et al³⁰ found that refining ML models by homogeneous subgroups can enhance the predictive capability of short-term postoperative outcomes (ie, 30-d readmission, reoperation, and prolonged length of stay) in patients who underwent autologous, implant-based, or tissue-expander reconstruction. ML models were trained on 27,718 patients. Across reconstruction types, ML model AUCs ranged from 0.61 to 0.86, with top performance for prolonged length of stay in implant cases (AUC = 0.86) and 30-day readmission in delayed autologous reconstructions (AUC = 0.86). Stratifying patient cohorts by reconstruction timing and laterality substantially improved AUCs: 30-day readmission increased from 0.71 in unstratified autologous cases to 0.86 in delayed bilateral autologous reconstructions.³⁰

Flap Perfusion

A 2024 retrospective feasibility analysis by Dalli et al³¹ demonstrated that ML could not only detect subtle flap perfusion deficits, which are normally dependent on subjective human observation, but also potentially support real-time intraoperative decision-making. The authors extracted quantitative perfusion metrics (ie, mean fluorescence intensity, flow speed) from intraoperative indocyanine green angiography videos during nipple-sparing mastectomies to develop predictive ML algorithms for perfusion-related complications. The model achieved 84.6% sensitivity, 76.9% specificity, and an AUC of 0.76 with an accuracy of 80.77%. It also produced a full-view perfusion heatmap using relevant quantitative data to visually display perfusion parameters.³¹

Tissue Expansion Modeling

A 2025 feasibility study by Laudo et al³² provided a proof-of-concept for predictive, computer-generated simulations that could eventually integrate ML to guide skin management in expander-based BR. The authors used serial 3D photographs and computed tomography scans of a 43-year-old woman undergoing postmastectomy tissue expansion to build a personalized computational model simulating skin deformation and growth. This “digital twin” incorporated biomechanical parameters derived from existing porcine data and was calibrated with the patient’s actual imaging and expansion data. The model reproduced the patient’s final postexpander skin deformation within 2 mm and skin growth within approximately 10% error.³² Although “digital twin” modeling is not yet a clinical reality, this study reveals the future potential of ML.

Path Forward: Aesthetic Outcomes

Two studies by Kenig et al^33,34 represented the next step toward AI-powered aesthetic evaluation systems for measuring outcomes in BR. One study used an ML model to automatically detect key breast landmarks, achieving more than 97% accuracy and markedly reducing assessment time, establishing a framework for objective symmetry analysis.³³ A subsequent 2025 study compared human evaluators, a custom YOLOv8 ML algorithm, and publicly available AI models in assessing postoperative breast symmetry among 68 patients.³⁴ YOLOv8 and ChatGPT-4o showed moderate-to-strong correlation with human assessments (r = 0.6-0.65), demonstrating that AI can approximate human judgment of aesthetic results.^33,34

DISCUSSION

This critical review consolidates current literature to characterize how AI is being developed to predict outcomes after BR. Although most studies demonstrated progress in AI-predicted postoperative outcomes, others identified promising directions for future development or revealed limitations in generalizability.

Most studies under the progress category demonstrated AUCs in the 0.7–0.9 range. Although AI developments are gradually approaching clinically meaningful applications such as predicting breast and donor-site complications, TE/implant loss, NAC or skin-flap necrosis, breast symmetry, and PROs, very few studies evaluated how these predictions translated to actionable changes in real-life surgical environments. In most studies, AI use was limited to identifying patients at higher risk of postoperative complications. Because no studies prospectively evaluated whether AI-informed predictions meaningfully altered surgeon behavior or patient outcomes, the reviewed studies should be considered early indicators of viable AI applications in reconstructive breast surgery, rather than ready-to-use clinical tools. Furthermore, although AI models have been shown to identify patients at a higher risk of complications, there is no evidence showing how that information could meaningfully alter management (eg, whether a different reconstructive method is more favorable). There was only 1 study in our review that was able to specify optimal thresholds for donor-site complications in DIEP flap patients.¹³ The absence of studies linking prediction to changes in management highlights a critical gap between model performance and real-world utility, underscoring the need for prospective studies in this area to evaluate whether AI can guide decision-making and improve critical outcomes.

Studies in the pitfalls category highlighted challenges with small datasets, generalizability, and technical limitations when studying things such as rare complications or infrequent operative outcomes with small datasets. Small sample sizes and rare event frequencies (eg, <2% flap loss in 1000+ patients) particularly restrict ML model stability, limiting reliability and generalizability to broader populations. In addition, the lack of standardized definitions for complications across different institutions may impact the generalizability of AI models developed at a single center. Until registries such as the NSQIP integrate AI-specific data collection and standardized outcome definitions, predictive algorithms will remain unreliable and single-center specific. This underscores the need for a shared AI data system within PRS to support uniform and scalable ML model development.

Beyond current constraints, the path forward studies demonstrated encouraging AI applications in predicting BR outcomes. Although studies under this category did not optimize their AI algorithms for individualized predictions, they illustrated how ML models detect nonlinear risk patterns across heterogeneous datasets. There was encouraging data regarding real-time intraoperative perfusion analysis and evidence that computational modeling could predict skin deformation in TE placement. With multicenter prospective validation and collaboration between surgeons and AI engineers, these advancements have the potential to greatly optimize postoperative success and clinical decision-making in reconstructive breast surgery.

Limitations

This review is limited by its descriptive nature, as well as the heterogeneity of the included studies and variables, which preclude quantitative meta-analysis. Additionally, the search strategy was limited to select databases and may not have captured all relevant studies. Despite these limitations, the structured progress, pitfalls, and path forward framework allowed for systematic interpretation of the current use of AI in predicting BR outcomes and identification of future opportunities for clinical application. As larger multicenter datasets emerge and AI tools begin integrating into clinical workflow, BR will be poised to benefit from increasingly personalized, data-driven surgical planning and outcome prediction.

CONCLUSIONS

Our findings show that AI is steadily moving from theoretical modeling to tangible applications within reconstructive breast surgery, such as predicting surgical complications, patient-reported satisfaction, and aesthetic outcomes. Although algorithmic performance seems promising, translation into real surgical practice is limited. Heterogeneity in procedures, surgical techniques, and nonstandardized outcome definitions makes it difficult to generalize algorithms across institutions and patient populations. Nonetheless, AI’s ability to capture complex relationships among patient, procedural, and aesthetic variables marks a pivotal step toward personalized reconstruction. Continued refinement through larger multicenter datasets, standardized outcome reporting, and surgeon–engineer collaboration will be essential to realize AI’s full potential in improving patient-specific care and surgical outcomes.

DISCLOSURES

Dr. Janis receives royalties from Thieme Medical Publishers and Springer Publishing. The other authors have no financial interest to declare in relation to the content of this article.

ACKNOWLEDGMENTS

AI-assisted digital tools were used solely for grammar and spelling. All study design, literature selection, data interpretation, and scientific content were developed by the authors.