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Extracting Clinical Recommendations from Oncology Guidelines: An Exploratory Comparison of Automated Approaches
Maurice Walny1, Sebastian Boie1, Stefan Haufe1,2,3
1Charité - Universitätsmedizin Berlin, Institute of Medical Informatics, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Clinical practice guideline recommendations are vital for patient care. Large language models (LLMs) and visual language models (VLMs) can extract most recommendations, but risks for future use persist.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Clinical practice guidelines (CPGs) are essential for evidence-based patient care.
- Automated extraction of CPG recommendations can improve accessibility and implementation.
- Current extraction methods may have limitations in accuracy and scope.
Purpose of the Study:
- To compare the effectiveness of Large Language Model (LLM) and Visual Language Model (VLM) based approaches for extracting recommendations from clinical practice guidelines.
- To evaluate these AI-driven methods against a traditional rule-based system.
- To identify the potential risks associated with automated recommendation extraction.
Main Methods:
- Development and application of LLM/VLM-based systems for recommendation extraction.
- Implementation of a rule-based system as a baseline for comparison.
- Quantitative analysis of extracted recommendations for accuracy and completeness.
Main Results:
- LLM and VLM approaches successfully extracted a significant majority of recommendations from clinical practice guidelines.
- The AI-based methods demonstrated comparable or superior performance to the rule-based baseline.
- Analysis revealed potential risks related to the interpretation and subsequent application of extracted recommendations.
Conclusions:
- LLM/VLM technologies show promise for automating the extraction of clinical practice guideline recommendations.
- While effective, careful consideration of potential risks is necessary for safe implementation.
- Further research is needed to mitigate risks and optimize AI-driven CPG recommendation extraction.
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