Plastic and reconstructive surgery. Global open · 2026
Artificial Intelligence and Robotic Surgery in Lymphedema: A Scoping Review of Current Applications, Clinical Translation, and Evidence Gaps.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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История · было → стало
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Fabi Adriano, Wendelspiess Séverin R, Klimitz Felix J, Zahedi Vafa Aliyar, Meroni Matteo, Alves André S, Haas Yvonne, Wallner Christoph, Haykal Siba, Kappos Elisabeth A
Аннотация
BACKGROUND: Lymphedema is a debilitating disease, in which timely diagnosis and standardized monitoring are essential to prevent progression. Artificial intelligence (AI) has increasingly been applied in other surgical disciplines. Therefore, this review summarizes current AI applications in lymphedema management and explores their potential for clinical translation.
METHODS: The PubMed/MEDLINE database was searched from inception to June 2025 for studies on lymphedema and AI. Peer-reviewed primary studies were included; editorials, reviews, and commentaries were excluded. All reference lists were screened for additional studies. Findings were synthesized qualitatively and grouped by clinical task.
RESULTS: Existing studies primarily evaluate AI for risk prediction, symptom- or image-based screening, automated volumetry, and lymphatic imaging interpretation. Reported performance is frequently high in single-center retrospective datasets; however, external validation and prospective workflow evaluation remain uncommon. Consequently, current applications are largely experimental and rarely used in clinical practice.
CONCLUSIONS: The integration of AI-assisted systems into the surgical management of chronic lymphedema has the potential to reduce diagnostic delays and to enable early therapeutic intervention. Additionally, it may improve surgical simulation, training, and real-time support in robotic (super-)microsurgery, thereby enhancing personalized patient care. However, translation to routine practice will require multicenter validation, transparent reporting, and demonstration of clinical utility, cost-effectiveness, and safety.
METHODS: The PubMed/MEDLINE database was searched from inception to June 2025 for studies on lymphedema and AI. Peer-reviewed primary studies were included; editorials, reviews, and commentaries were excluded. All reference lists were screened for additional studies. Findings were synthesized qualitatively and grouped by clinical task.
RESULTS: Existing studies primarily evaluate AI for risk prediction, symptom- or image-based screening, automated volumetry, and lymphatic imaging interpretation. Reported performance is frequently high in single-center retrospective datasets; however, external validation and prospective workflow evaluation remain uncommon. Consequently, current applications are largely experimental and rarely used in clinical practice.
CONCLUSIONS: The integration of AI-assisted systems into the surgical management of chronic lymphedema has the potential to reduce diagnostic delays and to enable early therapeutic intervention. Additionally, it may improve surgical simulation, training, and real-time support in robotic (super-)microsurgery, thereby enhancing personalized patient care. However, translation to routine practice will require multicenter validation, transparent reporting, and demonstration of clinical utility, cost-effectiveness, and safety.