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Page 129 Treger et al. Art Int Surg. 2025;5:126-32 https://dx.doi.org/10.20517/ais.2024.66

ADMINISTRATIVE EFFICIENCY
Administrative work accounts for approximately one-sixth of United States physicians’ working hours. It
[12]
has clearly demonstrated a negative impact on career satisfaction . Specific administrative tasks in plastic
surgery include but are not limited to documenting medical encounters, scheduling procedures and
operating room space, coordinating clinical appointments, communicating with ancillary care teams and
services, and facilitating insurance claims through accurate ICD coding. Healthcare costs associated with
[13]
administrative tasks have been estimated to contribute to 15%-25% of total healthcare spending .
Automating these tasks can not only lead to substantial cost savings for plastic surgery practices but can also
reduce the time burden associated with administration. Ultimately, this would enable plastic surgeons to
better focus their energy on attending to their patients.

Companies such as Microsoft and EPIC, the provider of a popular electronic medical record system, are
seeking to integrate large language models such as ChatGPT into patient charts. This collaboration aims to
address critical challenges such as clinician burnout, staffing shortages, and financial pressures within
healthcare systems. By leveraging Microsoft’s AI technical expertise alongside Epic’s electronic medical
record service, the partnership may enhance clinician productivity through tools such as note
summarization, ambient clinical documentation or scribing, and AI-powered data analysis . This
[14]
partnership is just one example of the numerous collaborations between healthcare services and AI
developers. Undoubtedly, efforts like this can enrich the overall efficiency of healthcare administration.

[15]
Medicare spending reached $944.3 billion in 2022, marking a 5.9% increase from the previous year . This
large financial flow presents significant opportunities for fraudulent activities. The sheer volume of
transactions can make it challenging to monitor and detect improper payments effectively. The Federal
Bureau of Investigation estimates that fraudulent billing accounts for three to ten percent of total health
spending . In order to combat this matter, machine learning engineers are currently exploring data-centric
[16]
approaches to healthcare fraud. Fraud classifications are being developed using data provided by the
Centers for Medicare & Medicaid Services (CMS) with the ultimate goal of developing AI fraud detection
[17]
tools . While these issues are not unique to plastic surgery, AI has the potential to encourage integrous
billing practices within the field and smoothen interactions between plastic surgeons and insurance
providers.

INSIDE THE OPERATING ROOM
Potential employments of AI in plastic surgery are vast and still currently being explored. With broad
applications across natural language processing, data analysis, deep learning, and computer vision, AI may
redefine the landscape of plastic surgery within the operating room. Preoperatively, AI may prove to be a
useful tool for surgical planning, ensuring consistency across patients . A focus of recent research has been
[18]
[19]
the tangible intraoperative application of AI through AI-driven surgical robots and navigation systems .
While significant advancements are required prior to their implementation, autonomous robots have the
potential to assist plastic surgeons in certain surgical tasks such as wound closure. This would enable
reliable results to be achieved, regardless of an individual’s surgical skill, and free up time for surgeons to
focus on other aspects of the operation. Three-dimensional overlays in augmented reality, possibly derived
from preoperative imaging, could assist surgeons in visualizing a patient’s anatomy . Such a tool might
[20]
help surgeons make informed decisions by highlighting critical structures and eliminating uncertainty
stemming from variations in patient anatomy. Postoperatively, AI systems may be a useful tool for
automated patient monitoring. For example, Fontaine et al. recently explored AI’s ability to evaluate
postoperative pain based on patient facial expression . The future of AI-assisted plastic surgery within the
[21]
context of operations remains unclear, but its successful incorporation holds promise of enhancing