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Rationale and objectives <p>To evaluate the impact of deploying AI as the first reader (R1) in a double-reading breast-screening workflow and to characterize second-reader (R2) behavior—including the effect of disclosing whether R1 was AI or human.</p> Materials and methods <p>This retrospective study used a cancer-enriched cohort of 220 women (95 cancers), with prevalence-weighted analyses performed to approximate population screening metrics. Five radiologists and one commercially available AI (Breast-SlimView<sup>®</sup>, Hera-MI) each served as R1; four radiologists served as R2. For each R2, cases were randomized 1:1 to AI-first versus human-first and, independently, to disclosure versus concealment of R1 identity. R2 could validate, dismiss, or add annotations. The primary endpoint was final decision correctness by breast. We used GEE logistic regression to estimate the overall effect of using AI as the first reader and to isolate second-reader behavior independently of first-reader accuracy.</p> Results <p>At the prespecified R1 operating point, AI had sensitivity/specificity/accuracy of 85.2%/79.5%/80.8% versus 84.3%/84.5%/85.0% for human R1s; crude final accuracy was lower for AI-first. At 0.6% prevalence, AI-first yielded higher recalls (20.8% vs. 16.8%) with slightly lower PPV (2.7% vs. 3.0%). Conditioning on R1 correctness, R2s were approximately twice more likely to overturn an incorrect AI-initiated opinion than an incorrect human-initiated one (OR ≈ 2.05, <i>p</i> &lt; 0.001). Disclosure that R1 was AI increased R2 corrections (from 13.6% to 19.1%, <i>p</i> = 0.029). Thirteen AI-true-positive cues were dismissed by R2.</p> Conclusions <p>At this operating point, AI-first reduced crude accuracy due to lower specificity, yet reader-behavior analyses indicate greater scrutiny of AI-initiated opinions. Protocol, threshold, and user-interface choices may raise specificity while preserving beneficial human–AI dynamics.</p>

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Human–AI interaction in a cancer-enriched double-reading breast screening cohort: diagnostic accuracy and second-reader behavior

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Eloïse Sossavi,
  • Mickaël Tardy,
  • Florie Hurstel,
  • Jean Schwartz,
  • Antoine Wackenthaler,
  • Claire Harter,
  • Julien Uttner,
  • Mélanie Mollion,
  • Marie-Françoise Bretz,
  • Sébastien Molière

摘要

Rationale and objectives

To evaluate the impact of deploying AI as the first reader (R1) in a double-reading breast-screening workflow and to characterize second-reader (R2) behavior—including the effect of disclosing whether R1 was AI or human.

Materials and methods

This retrospective study used a cancer-enriched cohort of 220 women (95 cancers), with prevalence-weighted analyses performed to approximate population screening metrics. Five radiologists and one commercially available AI (Breast-SlimView®, Hera-MI) each served as R1; four radiologists served as R2. For each R2, cases were randomized 1:1 to AI-first versus human-first and, independently, to disclosure versus concealment of R1 identity. R2 could validate, dismiss, or add annotations. The primary endpoint was final decision correctness by breast. We used GEE logistic regression to estimate the overall effect of using AI as the first reader and to isolate second-reader behavior independently of first-reader accuracy.

Results

At the prespecified R1 operating point, AI had sensitivity/specificity/accuracy of 85.2%/79.5%/80.8% versus 84.3%/84.5%/85.0% for human R1s; crude final accuracy was lower for AI-first. At 0.6% prevalence, AI-first yielded higher recalls (20.8% vs. 16.8%) with slightly lower PPV (2.7% vs. 3.0%). Conditioning on R1 correctness, R2s were approximately twice more likely to overturn an incorrect AI-initiated opinion than an incorrect human-initiated one (OR ≈ 2.05, p < 0.001). Disclosure that R1 was AI increased R2 corrections (from 13.6% to 19.1%, p = 0.029). Thirteen AI-true-positive cues were dismissed by R2.

Conclusions

At this operating point, AI-first reduced crude accuracy due to lower specificity, yet reader-behavior analyses indicate greater scrutiny of AI-initiated opinions. Protocol, threshold, and user-interface choices may raise specificity while preserving beneficial human–AI dynamics.