Advancing Evidence-Based Medicine for Population, Intervention, Comparison, and Outcome Element Recognition and Extraction in Medical Literature: Large Language Model Approach
Background: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract population, intervention, comparison, and outcome (PICO) elements—the foundational elements of evidence-based medicine. Objective: This study systematically evaluated 2 complementary approaches for automating PICO recognition and extraction in medical literature: prompt engineering optimization and parameter-efficient fine-tuning (PEFT) of large language models
Record details
Published: 14 August 2026
Source: JMIR (Journal of Medical Internet Research)
Category: Research
Topics: Healthcare
Retrieved: 15 August 2026
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How to cite this record
ethics.ai (14 August 2026), “Advancing Evidence-Based Medicine for Population, Intervention, Comparison, and Outcome Element Recognition and Extraction in Medical Literature: Large Language Model Approach,” evidence record 19560, https://ethics.ai/record/19560 (originally published by JMIR (Journal of Medical Internet Research)).
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