The Use of Emotional Artificial Intelligence in Plastic Surgery

Abstract

Background: The use of social media to discuss topics related to and within plastic surgery has become widespread in recent years; however, it remains unclear how to use this abundance of largely untapped data to propagate educational research in the field of plastic surgery. In this prospective, observational study, the authors aimed to delineate which plastic surgery–related topics evoked a significant emotional response within the study population and to assess the utility of motivational artificial intelligence within the field of plastic surgery. Methods: Over a 4-month period (January to April of 2018), Cognovi Lab’s artificial intelligence technology was used to search and analyze emotional reactions to several commonly hashtagged words. This innovative software uses several key metrics to describe its findings, including awareness, engagement, and motivation. Results: Of the search terms examined, “nose job” had the most awareness during the study period, and the topic that most engaged consumers emotionally was “liposuction.” Interestingly, “liposuction” ranked only fifth in terms of awareness. Consumers showed the strongest positive motivation toward the subjects of “plastic surgery” and “cosmetic surgery,” and the lowest motivation toward the topic of “tummy tucks.” Conclusions: This analysis by Cognovi Labs is the first quantitative effort to use the plethora of data on social media to interpret patient motivations and subsequent behavior. Moving forward, artificial intelligence technology will make it possible to predict which plastic surgery products, procedures, and practices will be successful. The findings presented in this article describe the unique viewpoint and power that this technology can deliver.

Artificial Intelligence in Plastic Surgery: Current Applications, Future Directions, and Ethical Implications

“… 16 A similar study by Cognovi Labs and Duke University used supervised ML technology to examine commonly hashtagged words associated with plastic surgery. 24 The authors suggest their results might inform decisions to use the title of aesthetic or cosmetic surgeon and emphasized the potential for such applications to influence marketing strategies and the public perception of plastic surgery. 16 , 24 …”

“… 24 The authors suggest their results might inform decisions to use the title of aesthetic or cosmetic surgeon and emphasized the potential for such applications to influence marketing strategies and the public perception of plastic surgery. 16 , 24 …”

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

“…The most frequently used algorithms were supervised machine learning (n = 11, 25%) 29 – 31 , 33 , 34 , 47 , 53 , 54 , 58 , 59 , 65 and artificial neural networks (n = 11, 25%). 36 , 40 , 42 , 43 , 50 , 57 , 60 – 64 Other algorithms used were convolutional neural networks (n = 8, 19%), 25 , 27 , 28 , 37 , 38 , 39 , 49 , 56 unsupervised machine learning (n = 4, 9%), 41 , 45 , 55 , 68 natural language processing (n = 4, 9%), 34 , 48 , 67 , 68 generative adversarial networks (n = 2, 5%), 17 , 26 computer vision (n = 2, 5%), 32 , 52 and combinations of models (combo; n = 2, 5%). 43 , 44 Input features were typically comprised of raw and preprocessed variables, such as subject characteristics (age, lapse time, comorbidities, vital signs, and laboratory values, anatomical and wound measurements, tissue reflectance spectrum), clinical images (facial photography, CT images, angiography, photoplethysmography, dermatoscopy, 3D cephalograms), surgical factors (surgical approach, intraoperative interactions with equipment), and synthetic or experimentally derived metrics (external muscle stimulation pulse widths, frequently asked questions).…”

“…Furthermore, only three studies used prospective data, 48 , 55 , 58 and five reported an area under the curve (AUC). 25 , 35 , 40 , 58 , 62 None of the papers were deficient in all nine quality criteria examined.…”

“…Most papers (n = 37, 85%) reported an accuracy of the AI model used, although three of these were inadequate and potentially biased. 29,31,63 Furthermore, only three studies used prospective data, 48,55,58 and five reported an area under the curve (AUC). 25,35,40,58,62 None of the papers were deficient in all nine quality criteria examined.…”