Scholarlink

Objectives <p>To assess how disclosing artificial intelligence (AI) results, particularly discordant findings, affects patient trust, anxiety, follow-up intentions, and attitudes toward AI in mammography. The study also assessed whether adding an explanatory note mitigates adverse reactions.</p> Materials and methods <p>A cross-sectional randomised experimental survey was conducted among 600 women (mean age 55.4 ± 6.8 years) undergoing mammography in two academic centres in Milan, Italy, between January 2023 and January 2024. Participants were randomised into four hypothetical BI-RADS 1 scenarios: Radiologist Only (control), AI No-Flag (AI concordant with radiologist), AI Flagged (AI discordant false-positive), and AI Flagged + Explanation (discordant AI with contextual information). Primary outcomes included trust (0–100 scale), worry, second-opinion intent, legal action intent, and AI approval. Analyses involved ANOVA, chi-square tests, and logistic regression with Bonferroni correction.</p> Results <p>Disclosure of a discordant AI result significantly reduced trust in the radiologist (73.0 vs 90.1; <i>p</i> &lt; 0.001), and increased anxiety (58.0% vs 16.0%; OR = 15.4), second-opinion intent (50.0% vs 8.7%; OR = 10.2), and legal action consideration (60.7% vs 38.7%; OR = 2.49). Adding explanatory context significantly mitigated these effects (e.g., anxiety: 25.3%; OR = 0.26). Compared to the Radiologist Only scenario, the AI Flagged + explanation scenario showed only a modest increase in anxiety (<i>p</i> = 0.04) and no significant trust reduction (<i>p</i> = 0.42). AI approval remained high (&gt; 85%) across all groups.</p> Conclusion <p>Disclosing discordant AI results reduces trust and increases anxiety, second-opinion intent, and legal concerns. Contextualised disclosure of AI results mitigates adverse emotional and behavioural responses, supporting its use as a communication strategy in AI-integrated mammography.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Current guidelines lack clear recommendations on disclosing AI-generated mammography findings, creating uncertainty about patient trust, anxiety, and medicolegal implications of discordant results</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>Disclosing discordant AI mammography findings reduced patient trust, increased anxiety, second-opinion seeking, and litigation intent; adding contextual explanations significantly mitigated these adverse effects</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Providing clear context about AI limitations in mammography reports mitigates patient anxiety, enhances trust in radiologists, and reduces unnecessary follow-up and potential medicolegal actions, supporting optimal patient communication during clinical implementation of AI</i>.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations

  • 期刊论文

  • 发表日期：

    2026年3月15日

查看全文
  • Filippo Pesapane,
  • Catherine Depretto,
  • Anna Rotili,
  • Silvia Penco,
  • Dario Monzani,
  • Roberto Grasso,
  • Luca Nicosia,
  • Carmen Mallardi,
  • Lucrezia D’Amelio,
  • Serena Carriero,
  • Giovanni Irmici,
  • Gianmarco Della Pepa,
  • Gabriella Pravettoni,
  • Sonia Santicchia,
  • Gianfranco Scaperrotta,
  • Enrico Cassano

摘要

Objectives

To assess how disclosing artificial intelligence (AI) results, particularly discordant findings, affects patient trust, anxiety, follow-up intentions, and attitudes toward AI in mammography. The study also assessed whether adding an explanatory note mitigates adverse reactions.

Materials and methods

A cross-sectional randomised experimental survey was conducted among 600 women (mean age 55.4 ± 6.8 years) undergoing mammography in two academic centres in Milan, Italy, between January 2023 and January 2024. Participants were randomised into four hypothetical BI-RADS 1 scenarios: Radiologist Only (control), AI No-Flag (AI concordant with radiologist), AI Flagged (AI discordant false-positive), and AI Flagged + Explanation (discordant AI with contextual information). Primary outcomes included trust (0–100 scale), worry, second-opinion intent, legal action intent, and AI approval. Analyses involved ANOVA, chi-square tests, and logistic regression with Bonferroni correction.

Results

Disclosure of a discordant AI result significantly reduced trust in the radiologist (73.0 vs 90.1; p < 0.001), and increased anxiety (58.0% vs 16.0%; OR = 15.4), second-opinion intent (50.0% vs 8.7%; OR = 10.2), and legal action consideration (60.7% vs 38.7%; OR = 2.49). Adding explanatory context significantly mitigated these effects (e.g., anxiety: 25.3%; OR = 0.26). Compared to the Radiologist Only scenario, the AI Flagged + explanation scenario showed only a modest increase in anxiety (p = 0.04) and no significant trust reduction (p = 0.42). AI approval remained high (> 85%) across all groups.

Conclusion

Disclosing discordant AI results reduces trust and increases anxiety, second-opinion intent, and legal concerns. Contextualised disclosure of AI results mitigates adverse emotional and behavioural responses, supporting its use as a communication strategy in AI-integrated mammography.

Key Points

Question Current guidelines lack clear recommendations on disclosing AI-generated mammography findings, creating uncertainty about patient trust, anxiety, and medicolegal implications of discordant results.

Findings Disclosing discordant AI mammography findings reduced patient trust, increased anxiety, second-opinion seeking, and litigation intent; adding contextual explanations significantly mitigated these adverse effects.

Clinical relevance Providing clear context about AI limitations in mammography reports mitigates patient anxiety, enhances trust in radiologists, and reduces unnecessary follow-up and potential medicolegal actions, supporting optimal patient communication during clinical implementation of AI.

Graphical Abstract