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Landau et al. Art Int Surg. 2025;5:24-35 https://dx.doi.org/10.20517/ais.2024.78 Page 26

Time spent on billing and administrative tasks largely detracts from the ability to allocate adequate time
toward patient care, whether in the clinical or operative setting, breeding burnout that heightens the risk of
medical errors. Mistakes not only lead to improper surgical outcomes, but also under-compensation for
surgical services [11,12] . With the utility of AI undergoing rapid improvement and continued integration into
the medical setting, the possibility of automating CPT code assignment by reading operative notes or
listening to dictations has piqued interest. Reducing administrative coding tasks can reallocate surgeons’
time to direct patient care, enhancing overall patient outcomes.

This review aims to synthesize insights from the literature on the deployment of AI, ML, and NLP
technologies for algorithm-dependent CPT code assignment applicable to the field of plastic and
reconstructive surgery. By examining metric-based performance, as well as current challenges and
advancements, this work leverages perspective on the transforming mode by which plastic and
reconstructive surgeons may engage with their practice and their patients, amidst a shifting technological
landscape in the broader surgical community.

METHODS
Study design
This review follows the standard protocol for preferred reporting items for systematic reviews and meta-
analyses, providing a general overview of the applicable existing literature. This study design was selected
with the goal of ultimately identifying gaps and growing opportunities for the utilization of AI, ML, and
NLP in the proper assignment of CPT codes for plastic surgery procedures.

Search strategy
A systematic search was conducted across three databases exclusively: PubMed, Scopus, and Web of Science
Core Collection. These databases were selected due to their extensive coverage of AI applications in
medicine. Database results were sought to collect a comprehensive pool of relevant peer-reviewed articles
published prior to September 10th, 2024. Search terminology included “Artificial Intelligence”, “Natural
Language Processing”, or “Machine Learning”, combined with “CPT” and “Surgery”.

Inclusion and exclusion criteria
The inclusion criteria for screening peer-reviewed articles from database searches focused on studies
examining the application of AI, ML, or NLP technologies in CPT coding for surgical procedures
commonly managed by plastic and reconstructive surgeons. The exclusion criteria for full-text screening
eliminated literature that did not specifically discuss the use of AI, ML, or NLP technologies in CPT code
assignment, opinion pieces or commentaries, and studies written in a non-English language without an
English translation available.

Data extraction
Data from the 11 identified studies were extracted using a standardized form that recorded authorship,
publication year, procedure or subspecialty area of plastic and reconstructive surgery, AI/ML/NLP methods
employed (with technology specified), coding accuracy results (with preservation of measurements in the
original study format), sample sizes (operative notes, patients, and/or procedures), involved CPT codes, and
key findings (unstructured qualitative). No additional variables were considered.

Quality assessment and data analysis
Two authors (M.L. and T.M.) independently screened peer-reviewed articles for eligibility, where each
author was blinded to the inclusion/exclusion decisions of the other until the screening process had
concluded. Peer-reviewed literature that then underwent data extraction was evaluated for adequate sample