Trial reportEuropean radiology2026
Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations.
Trial report in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Conditional Acceptance and the Optimism-Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy.Medical sciences (Basel, Switzerland) · 2026Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo 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
methodsA 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.
resultsDisclosure 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.
conclusionDisclosing 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.