Artificial Intelligence in Plastic Surgery

Current Applications, Future Directions, and Ethical Implications

Tyler Jarvis, BS; Danielle Thornburg, MD; Alanna M. Rebecca, MD, MBA; Chad M. Teven, MD

Disclosures

Plast Reconstr Surg Glob Open. 2020;8(10):e3200 

In This Article

Abstract and Introduction

Abstract

Background: Artificial intelligence (AI) in healthcare delivery has become an important area of research due to the rapid progression of technology, which has allowed the growth of many processes historically reliant upon human input. AI has become particularly important in plastic surgery in a variety of settings. This article highlights current applications of AI in plastic surgery and discusses future implications. We further detail ethical issues that may arise in the implementation of AI in plastic surgery.

Methods: We conducted a systematic literature review of all electronically available publications in the PubMed, Scopus, and Web of Science databases as of February 5, 2020. All returned publications regarding the application of AI in plastic surgery were considered for inclusion.

Results: Of the 89 novel articles returned, 14 satisfied inclusion and exclusion criteria. Articles procured from the references of those of the database search and those pertaining to historical and ethical implications were summarized when relevant.

Conclusions: Numerous applications of AI exist in plastic surgery. Big data, machine learning, deep learning, natural language processing, and facial recognition are examples of AI-based technology that plastic surgeons may utilize to advance their surgical practice. Like any evolving technology, however, the use of AI in healthcare raises important ethical issues, including patient autonomy and informed consent, confidentiality, and appropriate data use. Such considerations are significant, as high ethical standards are key to appropriate and longstanding implementation of AI.

Introduction

What is Artificial Intelligence?

For over 50 years, artificial intelligence (AI) has been an important topic of ongoing scholarship. In 1947, Alan Turing envisioned the concept of machines capable of human thought processes, stimulating groundbreaking research in AI and computer programming.[1,2] Later research in the 1960s and 1970s led to one of the most significant early applications of AI in medicine known as MYCIN.[3] The MYCIN system was trained to provide a differential for bacterial infections, which would include ranked probabilities for each possible microorganism and the recommended treatment.[3] In recent years, there have been strides in technologies designed for effectively diagnosing diseases and predicting outcomes using healthcare data.[4] It is conceivable that AI will be employed for the majority of diagnostic and decision-making processes in the near future, thus transforming healthcare as we know it.

AI encompasses a broad discipline of computer science that focuses on computer learning using large data sets. Recent progress in AI with regard to computational ability and ever-expanding data sets has been immense. AI can be used to create applications to complete tasks previously requiring a significant human input, improving efficiency in a wide range of fields. Several clinically applicable subdisciplines of AI include machine learning, deep learning, natural language processing, and facial recognition (Table 1).

Subdisciplines of Artificial Intelligence

Machine learning (ML) describes applications that can take data and uncover associations via pattern recognition among interacting variables.[5] Common applications of ML include classification and prediction models, which fall under the subcategory of supervised learning. Supervised learning models employ algorithms that are programmed to identify or predict an outcome using training data.[19] One example is a model that has been trained with electronic health record data to predict mortality of sepsis patients.[6] Facial recognition is subset of supervised ML. This technology has shown the potential to facilitate postoperative satisfaction in aesthetic surgery patients.[19] In contrast, unsupervised learning models require the discovery of novel associations in unlabeled data.[19] An example includes a model that organizes large amounts of genetic data by pattern recognition.[7]

Deep learning (DL) describes ML models that use artificial neural networks (ANNs) based on human brain function. These networks can improve their predictive performance and accuracy in classification with continued training using novel data.[5,9] The layered model structure supports the coupled extraction and analysis of the desired features within a data set.[20]

Models using natural language processing (NLP) and pattern recognition in unstructured data fall under the category of cognitive computing.[5] Unstructured data includes information that is generally more difficult to organize and analyze using traditional methods of statistical analysis, such as auditory and visual data.[9] Cognitive computing aims to complete tasks requiring the integration and organization of unstructured data to make decisions.

The technology underlying AI is progressing rapidly. Support from the scientific community and the federal government has generated the potential for AI to change healthcare delivery in profound ways.[21] Gradually, AI has been applied in plastic surgery, although limited literature exists. In this article, we summarize the application of AI in plastic surgery, highlighting current work and future directions. Additionally, we discuss important ethical implications concerning the use of AI in healthcare.

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