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from breast and chest surgery to facial or genital reconstruction, and the corresponding diversity of CPT
[32]
codes implicated highlight a potential target for application of these AI models, which performed well,
independent of the specialty-specific setting. Moreover, ChatGPT 4.0 with Bing accurately identified 75% of
appropriate CPT codes for craniofacial procedures, indicating the potential for these tools to expedite code
identification and enhance coding accuracy and efficiency , establishing a guideline for integration into
[14]
other areas of the specialty.

While the evident advantages of these technologies may seem unanimously agreeable, a shared
understanding of how their autonomous capabilities influence workflow within plastic and reconstructive
surgery exclusively is less certain. Cosmetic surgery remains subject to debate concerning the incorporation
of AI into medical coding practices, primarily due to the lack of insurance coverage for elective cosmetic
and aesthetic procedures. In such contexts, the AI methods described may serve little purpose, as
reimbursement may not be sought routinely, or at all, as with cash-pay practices where payment plans do
not invoke CPT code usage. Furthermore, plastic and reconstructive surgery procedures that harness both
reconstructive and cosmetic components display compatibility concerns, as any technology employed for
billing management would need to accurately distinguish between CPT codes for requested reimbursement
from those universally covered by the patient without the aid of medical insurance. The added complexity
of split insurance coverage found abundantly in plastic surgery necessitates higher-order AI development
using current models as a foundation to distinctly meet the needs of plastic and reconstructive surgeons.

Beyond initial implementation costs such as software licensing, hardware expenses, staff training, and data
migration, ongoing costs for subscription fees, system upgrades, and technical support services must also be
considered. For example, a 2011 study found that the cost of implementing a new EHR system for a five-
physician practice in the state of Texas cost an estimated $162,000, with $85,500 in maintenance expenses in
the first year . Nonetheless, trending initiatives streamlined by healthcare technology companies, such as
[33]
Epic Systems and Oracle Health, have incorporated generative AI into electronic medical record (EMR)
systems. Just last year, Epic announced their collaboration with Microsoft to integrate large language model
tools and AI into its EHR software; they additionally developed an AI tool with ambient voice technology to
aid in charting progress notes directly into the EHR after a patient exam, a feature available at 186
organizations as of April 2023 . Yet to be reported, another area of future investigation could include
[34]
evaluating differences in cost between implementing a new EHR system versus the cost of adding and
integrating AI technology for administrative purposes into the existing EHR. A 2017 WinterGreen market
shares report determined that as much as 88% of CPT code assignments for the purpose of billing and
reimbursement could occur automatically without any need for human review . This raises an important,
[35]
yet unanswered question: How much money could a healthcare system save on administrative costs by
implementing such technology and do these savings and trickle effects previously discussed justify the
upfront investment?

In terms of data security, the effectiveness of AI models is highly dependent on the quality and
representativeness of the training dataset: Poor-quality data can perpetuate existing biases, potentially
leading to disparities in care and ill-representative reimbursement for services provided. The 2023 report
from the Centers for Medicare and Medicaid Services on the Comprehensive Error Rate showed a 0.6% to
34.9% fee-for-service error rate, which was dependent on medical subspecialty . Therefore, it is imperative
[36]
to regard these novel integrative technologies as learning tools that must be trained prior to assisting with
definitive tasks and responding with efficient solutions. This calls for collaboration and investment from the
plastic surgery community to train such specialty-specific models and help ensure there are robust
regulations in place [37,38] .