Organ-on-a-chip (OoC) platforms are microfluidic systems that replicate key aspects of human tissue physiology in controlled environments. Originally developed for drug testing and disease modeling, they provide a more human-specific and reproducible alternative to traditional preclinical models, which often fail to capture the complexity of tissues relevant to plastic and reconstructive surgery.

This review synthesizes current OoC technologies with direct application to plastic and reconstructive surgery, focusing on skin-, vessel-, adipose-, and nerve-on-a-chip systems. The analysis emphasizes how these platforms model tissue structure, function, and interactions and evaluates their ability to simulate clinically relevant processes.

Skin-on-a-chip platforms enable dynamic modeling of wound healing, graft integration, and barrier function. Vessel-on-a-chip systems replicate microcirculatory flow, endothelial function, and vascular wall behaviors, supporting studies of flap viability and ischemia-reperfusion injury. Adipose-on-a-chip preserves lipid metabolism and inflammatory signaling, informing research into fat graft retention and remodeling. Nerve-on-a-chip platforms allow real-time monitoring of nerve injury and regeneration, aiding evaluation of nerve repair and graft performance. Across these systems, OoC models provide more clinically relevant insights than animal or static in vitro approaches, though limitations persist, including restricted physiological complexity, lack of platform standardization, short-term viability, and scalability challenges.

OoC platforms offer significant promise for advancing plastic and reconstructive surgery research by bridging translational gaps and aligning in vitro modeling with surgical outcomes such as graft take and nerve function. Future directions include incorporating immune elements, developing multitissue systems, expanding commercial accessibility, and improving long-term functionality. As these technologies mature, they have the potential to accelerate innovation and improve patient outcomes in reconstructive surgery.

Received: 19 January 2026

Accepted: 25 February 2026

Accepted Manuscript online:
03 March 2026

Article published online:
19 March 2026

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