⚡ Quick Summary
A recent study evaluated the effectiveness of GPT-4 Omni in transforming lobectomy surgical records into structured data across multiple languages. The results demonstrated an impressive accuracy of 0.966 and a F1-score of 0.982, highlighting the potential of AI to enhance documentation efficiency in thoracic surgical oncology.
🔍 Key Details
- 📊 Dataset: 466 lobectomy surgical records from seven specialized hospitals
- 🧩 Languages: Chinese and English
- ⚙️ Technology: Generative Pre-trained Transformer 4 Omni (GPT-4o)
- 🏆 Performance Metrics: Accuracy 0.966, Precision 0.981, Recall 0.982, F1-score 0.982
🔑 Key Takeaways
- 🤖 AI Transformation: GPT-4o effectively converts free text into structured surgical records.
- 💡 Multilingual Capability: The model performs well in both Chinese and English.
- ⏱️ Efficiency Gains: Significant reduction in documentation time compared to traditional methods.
- ⚠️ Error Types: Common errors included terminology misinterpretations (2.82%) and procedural sequence errors (1.41%).
- 🌟 Standardization Potential: GPT-4o could standardize surgical records and improve workflows.
- 🏥 Clinical Relevance: Enhancements in documentation can boost care and research in thoracic oncology.
- 🔍 Expert Review: Thoracic oncologists assessed the AI’s output for accuracy and precision.
📚 Background
The documentation of surgical procedures is crucial for patient care and research. However, traditional methods often lead to inefficiencies and inaccuracies. The integration of AI technologies, such as GPT-4 Omni, offers a promising solution to streamline the documentation process, particularly in complex fields like thoracic surgical oncology.
🗒️ Study
This multi-center study involved 466 lobectomy surgical records collected from seven specialized hospitals. The research aimed to assess the effectiveness of GPT-4o in transforming free text into structured data, thereby enhancing the documentation process. The study began with optical character recognition (OCR) and text normalization, followed by a manual restructuring by thoracic oncologists to establish a benchmark for the AI’s performance.
📈 Results
The performance of GPT-4o was remarkable, achieving an accuracy of 0.966, precision of 0.981, recall of 0.982, and an F1-score of 0.982 across both Chinese and English records. These metrics indicate a high level of effectiveness in transforming surgical records, significantly speeding up the documentation process compared to traditional methods.
🌍 Impact and Implications
The findings from this study suggest that GPT-4o has the potential to revolutionize the way surgical records are documented. By improving efficiency and accuracy, this technology could lead to better patient care and facilitate research in thoracic oncology. The ability to standardize records across languages also opens up new avenues for collaboration and data sharing in the global medical community.
🔮 Conclusion
This study highlights the transformative potential of AI in the field of surgical documentation. With its impressive performance metrics, GPT-4o stands out as a valuable tool for enhancing the accuracy and efficiency of surgical records. Continued refinement of AI technologies will be essential to address existing limitations and further improve contextual understanding in medical documentation. The future of surgical record-keeping looks promising with the integration of such advanced technologies!
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Cross language transformation of free text into structured lobectomy surgical records from a multi center study.
Abstract
In a recent study, the effectiveness of GPT-4 Omni in transforming lobectomy surgical records into structured data across multiple languages was explored. The aim was to improve both efficiency and accuracy in documenting thoracic surgical oncology procedures. Involving 466 records from seven specialized hospitals, the process started with OCR and text normalization. A manual restructuring by thoracic oncologists set the benchmark for fine-tuning Generative Pre-trained Transformer 4 Omni (GPT-4o). Experts reviewed the AI’s output, assessing it on accuracy, precision, recall, and F1 scores. GPT-4o demonstrated high performance across both Chinese and English records, achieving an accuracy of 0.966, precision of 0.981, recall of 0.982, and an F1-score of 0.982 in both language settings. Results showed that GPT-4o was highly effective in both Chinese and English, significantly speeding up documentation compared to traditional methods. While it performed well across languages and reduced review times, common error types included terminology misinterpretations (2.82%), procedural sequence errors (1.41%), and omissions of key details (0.47%). While it performed well across languages and reduced review times, these limitations highlight areas for further refinement, particularly in enhancing contextual understanding and mitigating minor errors. Nonetheless, GPT-4o shows great potential in standardizing surgical records, streamlining workflows, and boosting care and research in thoracic oncology.
Author: [‘Yang X’, ‘Xiao Y’, ‘Liu D’, ‘Deng H’, ‘Huang J’, ‘Zhou Y’, ‘Dai C’, ‘Wu J’, ‘Liu D’, ‘Liang M’, ‘Xu C’]
Journal: Sci Rep
Citation: Yang X, et al. Cross language transformation of free text into structured lobectomy surgical records from a multi center study. Cross language transformation of free text into structured lobectomy surgical records from a multi center study. 2025; 15:15417. doi: 10.1038/s41598-025-97500-7