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Landau et al. Art Int Surg. 2025;5:24-35 Artificial
DOI: 10.20517/ais.2024.78
Intelligence Surgery

Systematic Review Open Access

Revamping medical coding with AI: a systematic
review of interdisciplinary applications and

perspectives for plastic surgery

1,#
2
1
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Madeleine B. Landau 1 , Jared Rosbrugh , Kristen Rizzuto , Tatjana Mortell , Alexis Schlosser , Abigail
E. Chaffin 2
1
School of Medicine, Tulane University, New Orleans, LA 70112, USA.
2
Division of Plastic and Reconstructive Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, LA
70112, USA.
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Authors contributed equally.
Correspondence to: Madeleine B. Landau, School of Medicine, Tulane University, 1430 Tulane Ave., New Orleans, LA 70112,
USA. E-mail: mlandau@tulane.edu
How to cite this article: Landau MB, Rosbrugh J, Rizzuto K, Mortell T, Schlosser A, Chaffin AE. Revamping medical coding with
AI: a systematic review of interdisciplinary applications and perspectives for plastic surgery. Art Int Surg. 2025;5:24-35. https://
dx.doi.org/10.20517/ais.2024.78
Received: 16 Sep 2024 First Decision: 19 Nov 2024 Revised: 12 Dec 2024 Accepted: 17 Dec 2024 Published: 6 Jan 2025

Academic Editor: Andrew Gumbs Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Aim: This review evaluates the use of artificial intelligence (AI), machine learning (ML), and natural language
processing (NLP) technologies for enhancing current procedural terminology (CPT) coding accuracy and efficiency
in plastic and reconstructive surgery and related disciplines to define a precedent for future implementation.

Methods: A systematic search of PubMed, Scopus, and Web of Science Core Collection was performed to identify
studies that leveraged artificially intelligent technologies in coding related to surgical procedures commonly
managed by plastic and reconstructive surgeons.

Results: 11 peer-reviewed articles, which encompassed more than 1,000 CPT codes across numerous surgical
subspecialties with overlap in plastic and reconstructive surgery and model systems, were included. The key
findings highlight that AI-driven models demonstrate high sensitivity, specificity, area under the receiver operating
curve (AUROC), and accuracy. While performance metrics varied considerably depending on the specific AI model
employed, these systems were found to be effective assistive technologies for medical coding. Studies underscored
the advantages of integration, maximizing billing workflow and reducing administrative workload. However, studies

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
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