Artificial Intelligence and Robotic Surgery in Lymphedema: A Scoping Review of Current Applications, Clinical Translation, and Evidence Gaps.

Authors

Adriano Fabi, Séverin R Wendelspiess, Felix J Klimitz, Aliyar Zahedi Vafa, Matteo Meroni, André S Alves, Yvonne Haas, Christoph Wallner, Siba Haykal, Elisabeth A Kappos

Published in

Plastic and reconstructive surgery. Global open. Volume 14. Issue 9. Pages e8082. Epub Sep 02, 2026.

Abstract

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.
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.
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.
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.

PMID:
42689260
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.