Contextualized AI disclosure mitigates anxiety and trust loss in mammography patientsAI Disclosure Can Reduce Patient Anxiety During Mammogram Screenings

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.