Scholarlink

Background <p>Local flap reconstruction is a cornerstone of plastic surgery. It relies on the surgeon’s visual estimation and intraoperative judgement, which is gained through years of practical experience. Artificial Intelligence (AI) and machine learning present a novel opportunity to digitise surgical planning and to reduce subjectivity and optimise reconstructive design.</p> Methods <p>A narrative review was conducted using EMBASE and MEDLINE databases to identify studies investigating the application of AI in local flap surgery published within the last 15 years.</p> Results <p>Nine studies met the inclusion criteria. The current literature is characterised by high heterogeneity and is largely in the “proof-of-concept” stage. Preliminary evidence suggests that AI may assist in 2D to 3D visualisation and flap planning. However much of the data is translational.</p> Conclusions <p>AI integration shows potential to standardise decision making, optimise three-dimensional visualisation and reduce inter-operator variability. The field is still in its nascent stages and to transition from theoretical benefit to clinical utility the field demands robust research and safeguards that prioritise patient safety.</p> <p>Level of Evidence: not gradable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The digital era of reconstruction: artificial intelligence and the optimisation of local flaps

  • 期刊论文

  • 发表日期：

    2026年5月6日

查看全文
  • Anita Jacob,
  • Benedict Jong

摘要

Background

Local flap reconstruction is a cornerstone of plastic surgery. It relies on the surgeon’s visual estimation and intraoperative judgement, which is gained through years of practical experience. Artificial Intelligence (AI) and machine learning present a novel opportunity to digitise surgical planning and to reduce subjectivity and optimise reconstructive design.

Methods

A narrative review was conducted using EMBASE and MEDLINE databases to identify studies investigating the application of AI in local flap surgery published within the last 15 years.

Results

Nine studies met the inclusion criteria. The current literature is characterised by high heterogeneity and is largely in the “proof-of-concept” stage. Preliminary evidence suggests that AI may assist in 2D to 3D visualisation and flap planning. However much of the data is translational.

Conclusions

AI integration shows potential to standardise decision making, optimise three-dimensional visualisation and reduce inter-operator variability. The field is still in its nascent stages and to transition from theoretical benefit to clinical utility the field demands robust research and safeguards that prioritise patient safety.

Level of Evidence: not gradable.