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New research · Plastic Surgery
Plastic and reconstructive surgery. Global open · 2d
AI / informaticsPlastic and reconstructive surgery. Global open · 2026

Artificial Intelligence for Automated Current Procedural Terminology Coding in Plastic and Reconstructive Surgery Using a Fine-tuned Hybrid Large Language Model.

Daniel Oh, Jeffrey Cripe, James Baez-Silva … Kameron Rezzadeh
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Plastic SurgeryAI / informatics

ProCode AI achieved 100% accuracy for low-complexity CPT coding in plastic surgery.

Artificial Intelligence for Automated Current Procedural Terminology Coding in Plastic and Reconstructive Surgery Using a Fine-tuned Hybrid Large Language Model.

Daniel Oh et al. · Plastic and reconstructive surgery. Global open · 2026
Background

Current Procedural Terminology (CPT) coding in plastic and reconstructive surgery is complex, time-intensive, and prone to error, particularly for procedures involving multiple CPT codes.

Methods

A retrospective comparative study evaluated CPT code set accuracy across baseline LLMs (OpenAI, Gemini, Anthropic), external professional auditors, and a fine-tuned hybrid LLM system (ProCode), using expert consensus coding as the reference standard.

n = 40 cases
Results

The specialized AI system correctly identified all CPT codes for simple procedures.

100%
low
80%
medium
80%
high
More results

Baseline LLM accuracy declined noticeably with increasing complexity.

Differences in correctness by method were significant overall and within each complexity tier (all P < 0.05).

“
Conclusion · 1 of 2

A fine-tuned, domain-specific LLM substantially outperforms baseline LLMs and external professional auditors for automated CPT coding in plastic and reconstructive surgery, particularly for complex multicoded procedures.

Conclusion · 2 of 2

These findings support the role of specialized AI systems in improving coding accuracy and surgical billing workflows.

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