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               REFERENCES
               1.       Zhu C, Attaluri PK, Wirth PJ, Shaffrey EC, Friedrich JB, Rao VK. Current applications of artificial intelligence in billing practices and
                   clinical plastic surgery. Plast Reconstr Surg Glob Open. 2024;12:e5939.  DOI  PubMed  PMC
               2.       Esposito T, Reed R, Adams RC, Fakhry S, Carey D, Crandall ML. Acute care surgery billing, coding and documentation series part 1:
                   basic evaluation and management (E/M), emergency department E/M, prolonged services, adult critical care documentation and
                   coding. Trauma Surg Acute Care Open. 2020;5:e000578.  DOI  PubMed  PMC
                             ®
               3.       Dotson P. CPT  Codes: what are they, why are they necessary, and how are they developed? Adv Wound Care. 2013;2:583-7.  DOI
                   PubMed  PMC
               4.       Venkatesh KP, Raza MM, Kvedar JC. Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med.
                   2023;6:16.  DOI  PubMed  PMC
               5.       Abràmoff MD, Roehrenbeck C, Trujillo S, et al. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit Med.
                   2022;5:72.  DOI  PubMed  PMC
               6.       Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering.
                   2024;11:337.  DOI  PubMed  PMC
               7.       Khaleghi T, Murat A, Arslanturk S. A tree based approach for multi-class classification of surgical procedures using structured and
                   unstructured data. BMC Med Inform Decis Mak. 2021;21:328.  DOI  PubMed  PMC
               8.       Xu HA, Maccari B, Guillain H, Herzen J, Agri F, Raisaro JL. An end-to-end natural language processing application for prediction of
                   medical case coding complexity: algorithm development and validation. JMIR Med Inform. 2023;11:e38150.  DOI  PubMed  PMC
               9.       Jarvis T, Thornburg D, Rebecca AM, Teven CM. Artificial intelligence in plastic surgery: current applications, future directions, and
                   ethical implications. Plast Reconstr Surg Glob Open. 2020;8:e3200.  DOI  PubMed  PMC
               10.      Tavabi N, Singh M, Pruneski J, Kiapour AM. Systematic evaluation of common natural language processing techniques to codify
                   clinical notes. PLoS One. 2024;19:e0298892.  DOI  PubMed  PMC
               11.      Blanchfield BB, Heffernan JL, Osgood B, Sheehan RR, Meyer GS. Saving billions of dollars - and physicians’ time - by streamlining
                   billing practices. Health Aff. 2010;29:1248-54.  DOI  PubMed
               12.      Tseng P, Kaplan RS, Richman BD, Shah MA, Schulman KA. Administrative costs associated with physician billing and insurance-
                   related activities at an academic health care system. JAMA. 2018;319:691-7.  DOI  PubMed  PMC
               13.      Cheng CP, Sicard R, Vujovic D, et al. Replicating current procedural terminology code assignment of rhinology operative notes using
                   machine learning. World J Otorhinolaryngol Head Neck Surg. 2024.  DOI
               14.      Isch EL, Sarikonda A, Sambangi A, et al. Evaluating the efficacy of large language models in CPT coding for craniofacial surgery: a
                   comparative analysis. J Craniofac Surg. 2024.  DOI  PubMed
               15.      O’Malley GR Jr, Sarwar SA, Cassimatis ND, et al. Can publicly available artificial intelligence successfully identify current
                   procedural terminology codes for common procedures in neurosurgery? World Neurosurg. 2024;183:e860-70.  DOI
               16.      Zaidat B, Tang J, Arvind V, et al. Can a novel natural language processing model and artificial intelligence automatically generate
                   billing codes from spine surgical operative notes? Global Spine J. 2024;14:2022-30.  DOI  PubMed  PMC
               17.      Kim JS, Vivas A, Arvind V, et al. Can natural language processing and artificial intelligence automate the generation of billing codes
                   from operative note dictations? Global Spine J. 2023;13:1946-55.  DOI  PubMed  PMC
               18.      Shost MD, Meade SM, Steinmetz MP, Mroz TE, Habboub G. Surgical classification using natural language processing of informed
                   consent forms in spine surgery. Neurosurg Focus. 2023;54:E10.  DOI  PubMed
               19.      Zaidat B, Lahoti YS, Yu A, Mohamed KS, Cho SK, Kim JS. Artificially intelligent billing in spine surgery: an analysis of a large
                   language model. Global Spine J. 2023:21925682231224753.  DOI  PubMed
               20.      Kim JS, Arvind V, Schwartz JT, et al. P72. Natural language processing of operative note dictations to automatically generate CPT
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