Role of Artificial Intelligence (AI) in Plastic Surgery

Artificial intelligence (AI) has changed the world and has made social life easier and productive. It has helped in therapeutic decision making and also allows doctors to stay up-to-date. We have used Alexa™ as a tool of AI and has studied the applications in plastic surgery. We have found it to be useful and makes information procurement easier.

Artificial intelligence (AI) refers to machines capable of understanding and interpreting data, learning, and making decisions while adapting to circumstances [1]. The learning and problem-solving abilities of AI have been used for environmental monitoring, education, astronomy, and discovering new sources of energy [2,3]. Artificial intelligence and machine learning can be used in the medical field as both uses complex algorithms for management of a problem. Alexa™ is a voice assistant that was introduced by Amazon which has become very useful as it uses AI and machine learning for the daily activities. In this paper we are discussing about the application of Alexa™ as a tool of AI in plastic surgery.

This was a study conducted in the department of plastic surgery in a tertiary care hospital. The study was done by using Amazon Alexa™. The device is 9.91*9.91*4.21 cm in size and weighs 299.37g, and offers Wi-Fi connectivity and Bluetooth support. The device after switching on is used by downloading the Alexa™ app on smartphone or tablet and adding the device, and needs connection to internet to offer its services. The cost of the device is 3,500 to 9000 rupees. It was used by the patient side, by the nursing staff and by residents for various purposes from clearing doubts of patients, helping in dispensing of drugs, research work also. A detailed questionnaire was filled up by the patients, residents, nursing staff and assessment of the responses done (Table 1). 

Table 1: Proforma.

Alexa™ is found to be very useful in patient care. Patients also found interaction with Alexa™ useful and helpful. Residents found it useful in imparting patient care and research activities. The disadvantages were that it took time for getting accustomed to the voice response, time for machine learning also needs consideration. The cost factor also limits its widespread use.

Plastic surgeons often face clinical scenarios that have no clear-cut solution. Optimal treatment requires a decision model that integrates multiple influencing factors such as clinical and socio-demographic data but is also straight forward to use. Application of AI in plastic surgery starts from involvement in decision making in plastic surgery. Alexa™ can be used to make decision about various procedures as it involves machine learning and can be trained to respond to standard clinical scenarios by providing various verbal inputs. Machine learning (ML) includes multiple algorithms that learn from data and user response to achieve new insights about pre-existing data, develop descriptive models, and predict its outcomes [4]. It can help by searching for various standard guidelines on voice search especially in scenarios where decision has to be taken very fast in operation theatre where the hands may not be free. Traditionally, residents supplemented their clinical learning with lectures, conferences, and reading focused on evidence-based medicine. However, this may not be the most efficient format for learning due to the hectic clinical work, limited resources, and increasing amount of literature. AI can facilitate the acquisition of knowledge and reduce the time needed for extra clinical education. With the help of natural language processing, the burden of learning evidence based medicine can be shifted from residents alone to machine aided [5]. Alexa™ can be useful to residents (Figures 1-5) as it can search for research papers and necessary information can be sorted out, it can also integrate with various educational tools to allow for efficient resident training like Alexa™ linked operation theatre which light can help in surgery when single surgeon is operating. Patient care begins with symptom analysis, arriving at a diagnosis, making a treatment plan and making sure the patient adheres to the same. Alexa™ is an integral part of the social life of a large share of humans and it can understand even subtle changes in the human voice which may point to any disease like a common flu or malignancy of the speech apparatus and ask the patient to see a doctor or take a regular prescription depending on  the associated conditions of the patient. Alexa™ can remind about vaccination days, remind about medical appointments, drugs to be taken, follow up visits (Figure 2). Alexa™ can schedule meeting with family physicians, get prescription for monthly medications, make arrangements for medicines to be delivered door step. This is very important in old patients who suffer from various co –morbidities as well from Alzheimer’s or from poor memory.

Figure 1: ALEXA™.

Figure 2: Role of Alexa™ in patient care.

Figure 3: Role of Alexa™ in nursing care.

Figure 4: Role of Alexa™ in residency training.

Alexa™ can become an important part of providing healthcare provided it is used judiciously. Alexa™ can help the patient, surgeon, nursing staff, and resident in various fields of medicine. However detailed large scale studies may be needed to know how much Alexa™ can impact the practice of plastic surgery and its various uses in practice of medicine.

1. 1. Poole D, Mackworth A, Goebel R. Computational intelligence: a logical approach. Oxford University Press, Oxford. 1998.
   1. De Luna P, Wei J, Bengio Y, Aspuru-Guzik A, Sargent E. Use machine learning to find energy materials. Nature. 2017; 552: 23-27
   2. Musib M, Wang F, Tarselli MA, Yoho R, Yu KH, Andres RM, et al. Artificial intelligence in research. Science. 2017; 357: 28-30
   3. Kanevsky J, Corban J, Gaster R, Kanevsky A, Lin S, Gilardino M. Big data and machine learning in plastic surgery: a new frontier in surgical innovation. Plast Reconstr Surg. 2016; 137: 890-897.
   4. Moltaji S, Alkhatib AH, Liu H, Murphy J, Gallo L, Karpinski M, et al. Introducing knowledge translation to plastic surgery: turning evidence into practice. Plast Reconstr Surg. 2018; 142: 760-769.
   5. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18: 500-510.
   6. Yang D, Zhang S, Yan Z, Tan C, Li K, Metaxas D. Automated anatomical landmark detection ondistal femur surface using convolutional neural network. Biomedical Imaging (ISBI). In: 2015 IEEE 12^th international symposium on: IEEE. 2015; 17-21.
   7. O’Connell RL, Stevens RJ, Harris PA, Rusby JE. Review of three-dimensional (3D) surface imaging for oncoplastic, reconstructive and aesthetic breast surgery. Breast. 2015; 24: 331-342.