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Assessing AI Accuracy in Generating CPT Codes From Surgical Operative Notes.

Emily L Isch1, Judith Monzy2, Bhavana Thota2

  • 1Division of Plastic Surgery, Department of Surgery, Thomas Jefferson University.

The Journal of Craniofacial Surgery
|March 24, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise for automating Current Procedural Terminology (CPT) coding in craniofacial surgery. While both ChatGPT and Gemini performed similarly, AI integration could enhance medical billing efficiency and accuracy.

Keywords:
Artificial intelligenceCPT codescraniofacial surgerymedical codingnatural language processing

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Surgical Coding

Background:

  • Accurate medical coding is crucial for healthcare reimbursement and management.
  • Current Procedural Terminology (CPT) codes are vital for billing but prone to errors.
  • Large language models (LLMs) offer potential for automating medical coding tasks.

Purpose of the Study:

  • To evaluate the accuracy of LLMs in generating CPT codes for craniofacial surgical procedures.
  • To compare the performance of ChatGPT and Gemini in CPT code generation.

Main Methods:

  • Operative notes from 10 craniofacial cases were used.
  • AI tools (ChatGPT 4.0 and Gemini) generated CPT codes.
  • AI-generated codes were compared to expert manual coding for accuracy.

Main Results:

  • ChatGPT and Gemini showed comparable performance in CPT code generation.
  • No statistically significant difference in accuracy or correctness was observed (P > 0.999).
  • Gemini had more correct responses (30% vs. 20%), while ChatGPT had more partially correct responses (50% vs. 40%).

Conclusions:

  • AI tools show clinical value for craniofacial CPT coding.
  • AI can potentially reduce administrative burden and improve coding accuracy.
  • Future research should explore AI generalizability and model refinement for medical billing.