ArticleGlobal spine journal2025
Artificially Intelligent Billing in Spine Surgery: An Analysis of a Large Language Model.
Article in Global spine journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the Role of Large Language Models in Orthopedics: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Artificial Intelligence-Assisted Current Procedural Terminology Coding in Pediatric Orthopaedic Surgery: Promise or Premature?Journal of the Pediatric Orthopaedic Society of North America · 2026Article
- Large Language Models in Spine Surgery : A Narrative Review of Performance Paradox and Clinical Integration Challenges.Journal of Korean Neurosurgical Society · 2026Article
- Large Language Models in Spine Surgery: A Scoping Review of Clinical Efficacy, Technical Integration, and Ethical Paradigms.Global spine journal · 2026Review
- Comparison of proprietary and fine-tuned large language models for multi-label classification of billing codes from radiology reports.European radiology · 2026Article
- Large language models for zero-shot procedure extraction in orthopedic surgery: a comparative evaluation.Scientific reports · 2026Article
- Evaluation of Large Language Models for Automated SimpleFoot & ankle orthopaedics · 2026Article
- The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery.Current oncology (Toronto, Ont.) · 2025Review
- Large Language Models in Spine Surgery: A Promising Technology.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2025Review
- Large language models in perioperative medicine-applications and future prospects: a narrative review.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Review
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Implementation and Adoption of an Order-Based Surgical Case Request Tool across Subspecialty Clinics.Applied clinical informatics · 2025Article
- Artificial Intelligence in Surgical Coding: Evaluating Large Language Models for Current Procedural Terminology Accuracy in Hand Surgery.Journal of hand surgery global online · 2025Article
- Artificial Intelligence (AI) and Men's Health Clinic Efficiency and Clinic Billing.Current urology reports · 2024Review
- Prompt Engineering Paradigms for Medical Applications: Scoping Review.Journal of medical Internet research · 2024Article
- Letter to the Editor, "Artificially Intelligent Billing in Spine Surgery: An Analysis of a Large Language Model".Global spine journal · 2024Article
- Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designRetrospective cohort study.
objectivesThis study assessed the effectiveness of a popular large language model, ChatGPT-4, in predicting Current Procedural Terminology (CPT) codes from surgical operative notes. By employing a combination of prompt engineering, natural language processing (NLP), and machine learning techniques on standard operative notes, the study sought to enhance billing efficiency, optimize revenue collection, and reduce coding errors.
methodsThe model was given 3 different types of prompts for 50 surgical operative notes from 2 spine surgeons. The first trial was simply asking the model to generate CPT codes for a given OP note. The second trial included 3 OP notes and associated CPT codes to, and the third trial included a list of every possible CPT code in the dataset to prime the model. CPT codes generated by the model were compared to those generated by the billing department. Model evaluation was performed in the form of calculating the area under the ROC (AUROC), and area under precision-recall curves (AUPRC).
resultsThe trial that involved priming ChatGPT with a list of every possible CPT code performed the best, with an AUROC of .87 and an AUPRC of .67, and an AUROC of .81 and AUPRC of .76 when examining only the most common CPT codes.
conclusionsChatGPT-4 can aid in automating CPT billing from orthopedic surgery operative notes, driving down healthcare expenditures and enhancing billing code precision as the model evolves and fine-tuning becomes available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.