New Frontiers of Natural Language Processing in Surgery

Area of Science:

• Medical Informatics
• Computational Linguistics
• Surgical Research

Background:

• Electronic health records (EHR) generate vast amounts of data, posing challenges for traditional analysis.
• Non-machine learning data extraction methods are often inefficient and prone to errors.
• Advancements in Natural Language Processing (NLP) offer potential solutions for EHR data utilization.

Purpose of the Study:

• To review current perioperative applications of NLP in surgery.
• To identify limitations in existing NLP research for surgical data.
• To explore future directions for NLP in advancing surgical innovation.

Main Methods:

• Literature review of NLP applications in the perioperative setting.
• Analysis of research limitations and challenges.
• Identification of future research avenues and potential impact.

Main Results:

• NLP tools can automate and customize the review of EHR data for surgeons.
• Current applications focus on improving efficiency and enabling customized data analysis.
• Significant limitations exist in current NLP research, requiring further development.

Conclusions:

• NLP holds substantial potential to drive surgical innovation by unlocking the value of EHR data.
• Automated data extraction and analysis through NLP can significantly improve surgical research and practice.
• Further research is needed to overcome limitations and fully realize NLP's impact on surgical solutions.