Clinical Scorecard: Enhanced and Semi-Automated Creation of FESS Reports: A Proof-of-Concept Investigation

At a Glance

Key Highlights

• AI-based NLP tool developed to generate coherent, semi-automated surgical reports during FESS procedures.
• Model trained on 48 anonymized conventional FESS reports and 150 keywords linked to surgical steps.
• Potential to reduce physician workload and optimize clinical workflow by producing detailed reports in real-time.

Guideline-Based Recommendations

Diagnosis

• Not applicable; focus is on surgical report generation rather than diagnosis.

Management

• Implement NLP-based tools to assist in creating standardized, coherent surgical reports during FESS procedures.
• Use keyword-driven input during surgery to facilitate real-time report generation.

Monitoring & Follow-up

• Monitor accuracy and coherence of AI-generated reports compared to conventional reports.
• Evaluate integration impact on clinical workflow and physician time allocation.

Risks

• Potential errors in report generation if keyword input is incomplete or inaccurate.
• Need for expert validation to ensure clinical correctness of AI-generated content.

Patient & Prescribing Data

Patients undergoing functional endoscopic sinus surgery

AI-assisted report generation may indirectly improve patient care by allowing surgeons more time for direct patient management.

Clinical Best Practices

• Use expert-defined keywords linked to surgical steps to guide NLP report generation.
• Train language models on a diverse set of anonymized conventional surgical reports to capture typical language patterns.
• Incorporate augmentation techniques to improve model robustness against imbalanced data.
• Validate AI-generated reports with clinical experts before routine implementation.

References

• AI market in healthcare 2020
• NLP and AI applications in healthcare
• AI in diagnostic radiology
• Radiology report generation with AI
• SentencePiece tokenizer