Ethics for AI in Plastic Surgery: Guidelines and Review.

INTRODUCTION: Artificial intelligence (AI) holds the potential to revolutionize medicine, offering vast improvements for plastic surgery. While human physicians are limited to one lifetime of experience, AI is poised to soon surpass human capabilities, as it draws on limitless information and continuous learning abilities. Nevertheless, as AI becomes increasingly prevalent in this domain, it gives rise to critical ethical considerations that must be addressed by professionals. MATERIALS AND METHODS: This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application. RESULTS: Ethical challenges include the disclosure of use of AI by caregivers, validation of decision-making, data privacy, informed consent and autonomy, potential biases in AI systems, the opaque nature of AI models, questions of liability, and the need for regulations. CONCLUSIONS: There is a lack of consensus for the ethical use of AI in plastic surgery. Guidelines, such as those presented in this work, are needed within each discipline of medicine to respond to important ethical considerations for the safe use of AI. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A Novel Artificial Intelligence Model for Symmetry Evaluation in Breast Cancer Patients.

INTRODUCTION: Artificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has been mainly subjective, allowing for inconsistencies. This study aims to improve this evaluation process by integrating a novel trained neural network with the breast symmetry calculator, BAS-Calc. MATERIALS AND METHODS: We combined the BAS-Calc tool with a custom-made neural network trained to automatically detect key features of the breast. This integrated system was tested on 81 images of patients who had undergone breast reconstruction post-breast cancer treatment. Its performance was evaluated against two human observers using statistical analysis. RESULTS: Our model successfully detected 399/405 (98.51%) of landmarks. Spearman and Pearson correlation indicated a strong positive relationship while Cohen's kappa demonstrated moderate to strong agreement between human observers and AI model. Notably, the average calculation time for the AI was 0.92 seconds, 16 times faster than the 14.09 seconds for humans. CONCLUSIONS: Our AI model successfully calculated breast symmetry from images of patients who had undergone reconstructive oncological breast surgery, demonstrating high correlation with human assessments and a markedly reduced processing time. As AI continues to evolve, it is poised to become a pivotal tool in Medicine. Therefore, it is crucial for medical professionals to proactively engage in implementing AI technologies safely and effectively. Further studies are required to broaden our understanding and maximize the potential benefits in this area. Takeaway bullet points Artificial intelligence (AI) is an upcoming force to be reckoned with. AI should find its way into practical applications in plastic surgery. AI can be applied to improve patient care and evaluate aesthetic results. In this work, we present a novel AI model that automatically evaluates breast symmetry. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Identification of Key Breast Features Using a Neural Network: Applications of Machine Learning in the Clinical Setting of Plastic Surgery.

BACKGROUND: In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network. METHODS: An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery. RESULTS: The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds. CONCLUSIONS: The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.

Human Beauty according to Artificial Intelligence.

This work explores the upcoming era of artificial intelligence (AI), its potential impact on societal norms and aesthetics, and the biases inherent in AI systems. With the ability to generate realistic human-like art and language, AI entities like DALL·E or Midjourney have significant cultural and economic implications, particularly in creative sectors. However, our study highlights potential biases in AI, demonstrated through a text-to-image model called Craiyon, which was found to generate oversized and sexually suggestive images of breasts when prompted with certain phrases. These results underline the influence of societal norms on AI and the risk of perpetuating harmful stereotypes or unrealistic beauty standards. We emphasize the need for vigilance in monitoring AI's learning processes and potential biases, particularly as AI starts playing a crucial role in shaping societal perceptions of beauty and self. More inclusive and diverse AI models are needed to better represent the complexity of human beauty and to avoid biases.