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Page 25 Landau et al. Art Int Surg. 2025;5:24-35 https://dx.doi.org/10.20517/ais.2024.78

of AI performance for billing and medical coding within plastic surgery settings specifically were sparse. Notably,
these investigations emphasized the need to tailor models for targeted suitability.

Conclusions: This review highlights the potential of AI technologies to improve CPT coding by enabling time and
resource management and ultimately combatting the mounting presence of surgeon burnout. The sparsity of
plastic surgery-specific literature on this emerging topic and untested promise in the specialty calls for intentional
plastic surgeon-driven initiatives in the development of such applications.

Keywords: CPT, artificial intelligence, machine learning, natural language processing, surgery, billing,
reimbursement

INTRODUCTION
The innovative nature of plastic and reconstructive surgery makes the accurate assignment of current
procedural terminology (CPT) codes critical to ensure correct reimbursement, drive quality improvement,
and effectively manage surgical treatment . Medical coding in surgical departments has traditionally been a
[1]
complex process requiring detailed documentation, influenced by consistently changing coding guidelines.
CPT code assignment is an ongoing subject of debate, impacted by regulatory oversight and susceptible to
[2,3]
human error and inconsistencies .
Recent works have highlighted the challenges inherent in predicting CPT codes solely through manual
[4]
efforts, underscoring the need for novel solutions to enhance accuracy and efficiency in this domain .
Emerging technologies, particularly artificial intelligence (AI) applications utilizing machine learning (ML)
and natural language processing (NLP), have given way to an innovative frontier for improving coding
practices . New studies have documented the ability of various AI/ML/NLP methods to not only serve as
[5]
an administrative assistant, but also generate CPT codes based on patient operative notes in the hospital
[6]
setting .

ML is a subdivision of AI that relies upon algorithms and statistical models to enable computers to learn
from and make predictions or decisions based on input data. Instead of being explicitly programmed to
perform a task, ML systems are trained on a large amount of data, enabling them to improve their utility
through repetitive, automated decision-making processes. Common applications of ML include
recommendation systems, image recognition, and predictive analytics. For instance, studies employing a
random forest (RF) classification model have demonstrated the ability to achieve 74%-76% accuracy in
predicting primary CPT codes by integrating both structured and unstructured data . Furthermore,
[7]
advanced algorithms have shown the potential to enhance predictive performance by reorganizing possible
CPT codes based on identified key features, achieving a remarkable 20%-35% improvement in outcome
quality .
[7]

NLP promotes computers to comprehend, interpret, and produce human language in a way that is useful to
the user experience, including tasks such as language translation, sentiment analysis, speech recognition,
and text summarization. The application of NLP techniques has gained attention for their capability to
streamline and standardize the codification process, reducing the burden of substantiating manual coding
while minimizing the likelihood of potential error . Notably, analyses of common musculoskeletal CPT
[8,9]
codes indicate that traditional NLP approaches can outperform more complex models like BERT, achieving
an accuracy rate of 97% and offering crucial interpretability for clinical applications .
[10]