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Page 33 Landau et al. Art Int Surg. 2025;5:24-35 https://dx.doi.org/10.20517/ais.2024.78

These young methods for CPT coding also lend to the idea that such technology remains fallible to breaches
in cybersecurity, which can include unauthorized access to protected patient information. Ensuring
compliance with data privacy regulations like HIPAA is essential, which requires regulatory efforts by
medical institutions and greater failsafe measures by medical coders and developers of healthcare AI [14,15] .
Per prior recommendations for AI use in medical coding, regular audits and compliance checks should be
established alongside increased AI integration, both during the initial stages of development and piloting, as
well as throughout its long-term use, with scheduled checkpoints during routine use of these
[15]
technologies . Further, implementing encrypted data storage solutions and secure access protocols is
recommended to mitigate potential security breaches .
[15]
This study possesses several important limitations involving sample size, potential biases, and study rigor.
Due to the limited available literature that met eligibility criteria in this review, which resulted in a small
sample size of AI applications, generalizability is restricted. Furthermore, only a subset of our studies
demonstrated direct overlap in CPT codes, thus reducing the rigor of this work. Potential biases, exhibited
by the lack of unsuccessful instances of pilot AI model applications, also portray an area of weakness.
Nevertheless, this work strengthens a broader understanding of the current state of AI integration in billing
and medical coding within comparable settings, highlighting a present gap in the current literature, as well
as a feasible framework for understanding how current models could provide practical benefit to the
tailored administrative needs of plastic surgeons.

With enhanced efficiency and accuracy established across multiple domains of surgical practice and prior
work demonstrating high skill and procedural transferability , these same AI models applied in other
[39]
contexts have the potential to interpret complex operative notes characteristic of this specialty for the
purpose of CPT code assignment. Future work should embrace existing AI methods to assess their
performance in the plastic surgery setting. As these technologies become more affordable and accessible,
even integrated as a default extension of EMR systems nationally, it is imperative that plastic surgeons
explore the functional role of AI in billing and administrative management. Plastic and reconstructive
surgeons must strive to trial medical coding with AI, documenting the effects of training on large datasets
and de-identified clinical operative notes to actively keep pace with technological innovation, mitigating
burnout and improving the quality of surgical management.

DECLARATIONS
Acknowledgments
The authors appreciate the authors of both included peer-review studies and gray literature for the
provision of their investigation and insight from which this review was cultivated.

Authors’ contributions
Conception and design: Landau MB, Mortell T, Chaffin AE
Administrative support: Landau MB, Schlosser A, Chaffin AE
Collection and assembly of data: Landau MB, Rosbrugh J, Rizzuto K
Data analysis and interpretation: Landau MB, Mortell T, Rosbrugh J, Rizzuto K
Manuscript writing: Landau MB, Rosbrugh J, Rizzuto K, Mortell T, Schlosser A, Chaffin AE
Manuscript editing: Landau MB, Mortell T, Schlosser A
Final approval of manuscript: Landau MB, Rosbrugh J, Rizzuto K, Mortell T, Schlosser A, Chaffin AE

Availability of data and materials
Data needed are available within the manuscript and publicly available online.