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Evaluating Large Language Models for Radiology Systematic Review Title and Abstract Screening.

Siddhant Dogra1, Soroush Arabshahi2, Jason Wei1

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Large language models (LLMs) show promise for radiology systematic review screening, but require careful prompt design and human oversight for robust performance. Their decision-making behavior and confidence calibration need further evaluation.

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Area of Science:

  • Artificial Intelligence in Medical Research
  • Radiology Systematic Reviews
  • Natural Language Processing Applications

Background:

  • Systematic reviews are crucial for evidence synthesis in radiology.
  • Automating title and abstract screening with large language models (LLMs) could enhance efficiency.
  • Evaluating LLM performance, stability, and decision-making in this context is essential.

Purpose of the Study:

  • To assess the performance, stability, and decision-making of various LLMs for radiology systematic review screening.
  • To investigate the impact of prompt framing and confidence calibration on LLM accuracy.
  • To evaluate LLM robustness when faced with disagreements and explore autonomous search capabilities.

Main Methods:

  • Five LLMs (GPT-4o, GPT-4o mini, Gemini 1.5 Pro, Gemini 2.0 Flash, Llama 3.3 70B) were compared on two radiology systematic reviews.
  • Tasks included binary/ternary classification, confidence scoring, and reclassification of disagreements.
  • Autonomous PubMed retrieval was piloted using OpenAI and Gemini Deep Research tools.

Main Results:

  • LLMs demonstrated high specificity but variable sensitivity, with F1 scores ranging from 0.389 to 0.854.
  • Ternary classification yielded low abstention rates (<5%) and modest sensitivity gains.
  • Models exhibited authority bias, favoring human labels in disagreements, though GPT-4o showed more resistance.

Conclusions:

  • LLMs show potential for systematic review screening in radiology.
  • Careful prompt engineering and human-in-the-loop oversight are critical for reliable LLM application.
  • Further research is needed to optimize LLM integration and address decision-making biases.