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Purpose <p>Large language models (LLMs) show promise in radiological diagnosis, but their performance may be affected by the context of the cases presented. Our purpose is to investigate how providing information about prior probabilities influences the diagnostic performance of an LLM in radiological quiz cases.</p> Materials and methods <p>We analyzed 322 consecutive cases from Radiology’s “Diagnosis Please” quiz using Claude 3.5 Sonnet under three conditions: without context (Condition 1), informed as quiz cases (Condition 2), and presented as primary care cases (Condition 3). Diagnostic accuracy was compared using McNemar’s test.</p> Results <p>The overall accuracy rate significantly improved in Condition 2 compared to Condition 1 (70.2% vs. 64.9%, <i>p</i> = 0.029). Conversely, the accuracy rate significantly decreased in Condition 3 compared to Condition 1&#xa0;(59.9% vs. 64.9%, <i>p</i> = 0.027).</p> Conclusions <p>Providing information that may influence prior probabilities significantly affects the diagnostic performance of the LLM in radiological cases. This suggests that LLMs may incorporate Bayesian-like principles and adjust the weighting of their diagnostic responses based on prior information, highlighting the potential for optimizing LLM’s performance in clinical settings by providing relevant contextual information.</p>

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Influence of prior probability information on large language model performance in radiological diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月5日

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  • Takahiro Fukushima,
  • Ryo Kurokawa,
  • Akifumi Hagiwara,
  • Yuki Sonoda,
  • Yusuke Asari,
  • Mariko Kurokawa,
  • Jun Kanzawa,
  • Wataru Gonoi,
  • Osamu Abe

摘要

Purpose

Large language models (LLMs) show promise in radiological diagnosis, but their performance may be affected by the context of the cases presented. Our purpose is to investigate how providing information about prior probabilities influences the diagnostic performance of an LLM in radiological quiz cases.

Materials and methods

We analyzed 322 consecutive cases from Radiology’s “Diagnosis Please” quiz using Claude 3.5 Sonnet under three conditions: without context (Condition 1), informed as quiz cases (Condition 2), and presented as primary care cases (Condition 3). Diagnostic accuracy was compared using McNemar’s test.

Results

The overall accuracy rate significantly improved in Condition 2 compared to Condition 1 (70.2% vs. 64.9%, p = 0.029). Conversely, the accuracy rate significantly decreased in Condition 3 compared to Condition 1 (59.9% vs. 64.9%, p = 0.027).

Conclusions

Providing information that may influence prior probabilities significantly affects the diagnostic performance of the LLM in radiological cases. This suggests that LLMs may incorporate Bayesian-like principles and adjust the weighting of their diagnostic responses based on prior information, highlighting the potential for optimizing LLM’s performance in clinical settings by providing relevant contextual information.