Related Experiment Videos
Automated Summarization of Military Medical Records Using AI: Evaluation Via the OPTICA Framework
Yigal Chechik1, Yoav Jordan Gutterman1, Shlomi Abuhasira1
1Medical Corps, Israel Defense Forces, Ramat Gan 5262000, Israel.
Military Medicine
|June 23, 2026
Summary
Artificial intelligence (AI) medical record summarization tools can enhance military fitness evaluations by improving efficiency and reducing physician burden. This pilot study shows AI effectively supports clinicians in reviewing complex cases.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Decision Support Systems
Background:
- Military medical fitness evaluations are complex, time-consuming, and cognitively demanding for physicians.
- Existing processes contribute to inefficiencies and physician burnout due to extensive medical record review.
- Artificial intelligence (AI), specifically large language models (LLMs), presents a potential solution for streamlining these evaluations.
Purpose of the Study:
- To develop and pilot an AI-based system for automatic medical record summarization in military fitness evaluations.
- To assess the feasibility and impact of AI summarization on clinical workflow efficiency and physician cognitive load.
- To evaluate the system's performance using the OPTICA framework for clinical relevance, accuracy, completeness, and usability.
Main Methods:
- A prospective implementation case study was conducted over 6 months.
- An AI system integrating OCR, document intelligence, and LLMs was developed to process heterogeneous medical documents.
- The system processed over 12,000 reservist cases, with summaries reviewed by military physicians in routine workflows.
Main Results:
- The AI system successfully processed over 12,000 cases, generating structured summaries integrated into military clinical workflows.
- Physicians reported summaries were clinically relevant, facilitating faster understanding and reducing manual data extraction.
- The system demonstrated improved efficiency and perceived cognitive load reduction for physicians, functioning as a decision-support tool.
Conclusions:
- AI-based medical record summarization meaningfully supports military medical fitness evaluations by enhancing efficiency and reducing clinician burden.
- The pilot study confirms the feasibility of integrating LLM-based summarization into real-world military medical workflows.
- Further research is needed to evaluate long-term impact, scalability, and human-AI collaboration in high-stakes medical environments.
