Big Data and Precision Medicine
Precision medicine has the potential to revolutionize healthcare by treating patients as their own unique entity. The practice of precision medicine extends beyond traditional, population-based medicine and considers an individual's unique genetic and environmental components (Figure 1). For example, instead of assigning a common treatment for a certain disease, oncologists now analyze a patient's biopsy tissue for a panel of genetic variants, which enables a more reliable prediction of the patient's response to a particular treatment. With the availability of digital health records, at-home genetic screening, and demographic data, precision medicine may be implemented in a much broader format. Furthermore, big data techniques can enhance the practice of precision medicine with quick analysis, including rapid quantification of data and pattern recognition. Not only does this expand the capacity for information storage, but it enhances the ability to analyze, interpret, and make decisions with one click of a button. Figure 2 depicts how big data analytics can be used to implement precision medicine in clinical care.
Current technology in the form of wearable health-monitoring devices, such as Apple Watches, has the potential to hold a sheer volume of health data, such as heart rate and blood pressure. The most recent advancement of the Apple Watch will feature an application that can generate an electrocardiogram to be shared with a doctor at any time. Although these advances have the potential to be used for personal purposes, they will also contribute an immense amount of data to researchers. For instance, Stanford Medicine has partnered with Apple to use the data that are collected via the Apple Heart Study application to identify and notify patients of irregular heart rhythms. Not only will this study alert participants of potential serious heart conditions, but it will contribute a large amount of data to be used as "big data." In addition, a large source of big data is electronic health records (EHRs). The intended purpose of EHRs is to assist in the rapid retrieval of information and to augment skills such as patient–physician communication.[18–20] Although EHRs can theoretically provide instant and potentially organized access to patient information, the vast amount of information embedded into these digital records is often difficult to sort and apply expediently. Thus, the integration of big data analytics, such as artificial neural networks (ANNs), could provide a quick analysis of data to provide an output that is coherent and meaningful. ANNs are computing systems that mimic the biological neural network of a human brain, processing signals and making connections within data. ANNs use an algorithm to quantify and organize a set of data to recognize patterns that otherwise would be missed by a human. Figure 3 demonstrates the process of ANNs, showing the input, hidden, and output layers. The input layer represents the data that are inserted into the model, whereas the output layer represents the predictions that are made by the model. Most importantly, the hidden layer represents the algorithmic layer that recognizes any patterns within the data. Researchers in the field of biomedicine have already integrated big data analytics to extract, summarize, and interpret knowledge from rapidly generated data, which has improved accuracy in predictive modeling. Introducing this digital technology to surgery would not only give surgeons quick access to the information stored in EHRs, but they will have insight into any patterns recognized by ANNs to aid them in their decision-making process.
Within the past few years, AI has established a niche in different areas in the field of surgery. For example, researchers have investigated the use of robotics and AI to assist surgeons in keyhole neurosurgery. Furthermore, a robotic system equipped with AI algorithms was designed to perform ex vivo and in vivo bowel anastomosis. This study showed that surgical tasks that require human skills, such as dexterity and cognition, can indeed be programmed and executed with robotic systems. Additionally, surgeons have used AI-assisted surgery to perform sutures on small blood vessels in a patient who was suffering from lymphedema. The robot, manually controlled by a surgeon, demonstrated its ability to make precise movements and stabilize any tremors in the surgeon's movements. Although these techniques have not yet been applied to plastic surgery, this does not mean we cannot imagine AI-assisted surgery in the procedures that we perform. As plastic surgeons, it is now time for us to leverage our creativity to embrace these technological developments to advance our field and provide better care for our patients.
Plast Reconstr Surg Glob Open. 2019;7(3):e2113 © 2019 Lippincott Williams & Wilkins