Background:
Artificial intelligence (AI) is the simulation of human intelligence by machines and encompasses machine learning (ML), artificial neural networks (ANNs), and natural language processing (NLP). These enable machines to learn data, recognise patterns and analyse large volumes of clinical text. AI is becoming increasingly integrated into plastic surgery. In breast reconstruction, AI is developing rapidly and transforming postoperative care. Clinicians and institutions can benefit from employing AI through improved efficiency, enhanced clinical accuracy and improved patient outcomes. This review explores the current state of AI applications in postoperative breast reconstruction, including its use in predicting surgical complications, enhancing imaging interpretation, optimising follow-up care and guiding clinical decisions.
Methods:
This scoping review was conducted using a comprehensive search of PubMed, Google Scholar and Web of Science. Studies were selected based on their focus on AI-driven models in postoperative breast reconstruction.
Results:
AI-driven models have demonstrated strong performance in predicting postoperative breast reconstruction complications, outperforming traditional risk assessment methods. These predictive models stratify patient risk and tailor postoperative care, potentially reducing hospital stays and improving outcomes. AI enhances postoperative imaging by distinguishing normal healing from early pathology. AI subsets have been used to extract valuable insights from electronic health records, and predictive analytics can guide personalised patient follow-up care.
Nonetheless, utilisation of AI presents challenges including limited prospective validation, data security concerns, algorithm bias and lack of standardised datasets. Real-world implementation and ethical integration into clinical workflows are crucial next steps to ensure the effectiveness and safety of AI in clinical practice.
Conclusion:
AI can enhance postoperative breast reconstruction by improving predictive accuracy, imaging interpretation and personalised follow-up care. However, wider implementation requires addressing challenges including validation, data security, and ethical integration to ensure safe and effective adoption in clinical practice.