Skin, scalpel and the silicon chip: a systematic review on the accuracy, bias and data governance of artificial intelligence in dermatology, minimally invasive aesthetics, aesthetic, plastic and reconstructive surgery

Introduction Artificial Intelligence (AI) is becoming increasingly integrated into healthcare, particularly in fields like dermatology, minimally invasive aesthetics, aesthetic surgery, and plastic and reconstructive surgery. AI has the potential to improve diagnostic accuracy, personalised treatment, and patient outcomes. However, issues such as algorithmic bias, ethical concerns, and generalisability of models remain significant barriers to its full adoption in clinical practice. Methods A systematic review was conducted following PRISMA guidelines. A broad search of databases including PubMed, EMBASE, and Scopus was performed to identify studies on AI applications in dermatology, aesthetic treatments, and surgery. Inclusion criteria focused on studies evaluating AI’s impact on clinical outcomes, bias mitigation strategies, and data management. Data extraction and quality assessment were carried out by two independent reviewers. Results Out of 103 included studies, AI showed varying accuracy across different fields. In dermatology, AI models, particularly neural networks, achieved an average accuracy of 90%, while in minimally invasive aesthetics and aesthetic surgery, accuracy ranged between 85% and 95%. Bayesian analysis demonstrated a posterior probability of 0.78 that AI outperforms traditional methods. However, challenges in bias, particularly regarding dataset diversity and ethical concerns, were frequently noted, limiting generalisability and applicability across diverse populations. Conclusions AI offers significant promise in enhancing clinical outcomes, particularly in dermatology and aesthetic surgery. Nonetheless, biases and ethical issues must be systematically addressed. Further research and standardisation are needed to ensure AI’s responsible integration into healthcare.<p></p>