Accessing AI mammography reports impacts patient follow-up behaviors: the unintended consequences of including AI in patient portals

Although artificial intelligence (AI) tools are growing in breast imaging, little research has examined how to communicate AI findings in patient portals. English-speaking US women with ≥1 prior mammogram (n = 1623) were randomized to one of thirteen conditions. All participants viewed a radiologist report negative for breast cancer; twelve conditions included an AI report with one of four AI scores (0, 29 [no suspicion of cancer]; 31, 50 [suspicion of cancer]), presented alone, with an abnormality cutoff threshold, or with both the threshold and the AI's False Discovery Rate (FDR) or False Omission Rate (FOR). Participants reported whether they would consult an attorney for litigation if advanced cancer were detected one year later (primary outcome). Secondary outcomes included follow-up decisions, concern for cancer, and trust. Litigation was higher when AI was included, especially when AI concluded suspicion of cancer. Providing the FDR/FOR reduced litigation; similar effects were observed for follow-up behaviors.

Authors

Institutions

Publication Details

Journal
npj Digital Medicine
Published
2025-12-03
DOI
https://doi.org/10.1038/s41746-025-02201-0
Citations
5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
3.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accessing AI mammography reports impacts patient follow-up behaviors: the unintended consequences of including AI in patient portals

Elizabeth C. Song, Ana P. Lourenço, Michael H. Bernstein, Parker S. Lay et al.
5 citations
npj Digital Medicine
Artificial Intelligence in Healthcare and Education
3.27
article

Accessing AI mammography reports impacts patient follow-up behaviors: the unintended consequences of including AI in patient portals

Elizabeth C. Song, Ana P. Lourenço, Michael H. Bernstein, Parker S. Lay, Grayson L. Baird, Léo Druart, Elizabeth H. Dibble
article en
5 citations

Abstract

Although artificial intelligence (AI) tools are growing in breast imaging, little research has examined how to communicate AI findings in patient portals. English-speaking US women with ≥1 prior mammogram (n = 1623) were randomized to one of thirteen conditions. All participants viewed a radiologist report negative for breast cancer; twelve conditions included an AI report with one of four AI scores (0, 29 [no suspicion of cancer]; 31, 50 [suspicion of cancer]), presented alone, with an abnormality cutoff threshold, or with both the threshold and the AI's False Discovery Rate (FDR) or False Omission Rate (FOR). Participants reported whether they would consult an attorney for litigation if advanced cancer were detected one year later (primary outcome). Secondary outcomes included follow-up decisions, concern for cancer, and trust. Litigation was higher when AI was included, especially when AI concluded suspicion of cancer. Providing the FDR/FOR reduced litigation; similar effects were observed for follow-up behaviors.

npj Digital MedicineVol. 8(1)
Brown University (US), Rhode Island Hospital (US)
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare and Education
3.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Accessing AI mammography reports impacts patient follow-up behaviors: the unintended consequences of including AI in patient portals — Elizabeth C. Song, Ana P. Lourenço, et al. · npj Digital Medicine (2025) | TGRS Research Map | TGRS