Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study

This is the first study in Asia to evaluate AI assistance in mammography interpretation by radiologists of varying experience. AI significantly improved diagnostic performance and efficiency among residents, helping to narrow the experience-performance gap without compromising specificity. These findings suggest a role for AI in enhancing diagnostic consistency, improving workflow, and supporting training. Integration into clinical and educational settings may offer scalable benefits, though careful attention to threshold calibration, feedback loops, and real-world validation remains essential. Further studies in routine screening settings are needed to confirm generalizability and cost-effectiveness.

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Publication Details

Journal
JMIR Formative Research
Published
2025-11-24
DOI
https://doi.org/10.2196/66931
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
2.48
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Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study

Pooja Jagmohan, Felicity Jane Pool, Wei Chuan Tan, Vanessa Meihui Khoo et al.
1 citations
JMIR Formative Research
AI in cancer detection
2.48
article

Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study

Pooja Jagmohan, Felicity Jane Pool, Wei Chuan Tan, Vanessa Meihui Khoo, Mikael Hartman, Yun Song Choo, Mengling Feng, E. Seah, Desmond Shi Wei Lim, Shuliang Ge, M.J. Khaw, Hao Du, Serene Si Ning Goh, Samuel Lau, Chee Woei Yap, Han Yang Ong, Yi Wang, Wai Keat Lau, Kei Yiu Douglas Hui, Patinharayil Gopinathan, Alvin Hong Zhi Ng, Yi Liang Tan, Loon Ying Tan, Haziz Siti Rozana Binti Abdul
article en
1 citations

Abstract

This is the first study in Asia to evaluate AI assistance in mammography interpretation by radiologists of varying experience. AI significantly improved diagnostic performance and efficiency among residents, helping to narrow the experience-performance gap without compromising specificity. These findings suggest a role for AI in enhancing diagnostic consistency, improving workflow, and supporting training. Integration into clinical and educational settings may offer scalable benefits, though careful attention to threshold calibration, feedback loops, and real-world validation remains essential. Further studies in routine screening settings are needed to confirm generalizability and cost-effectiveness.

JMIR Formative ResearchVol. 9
National University of Singapore (SG), National University Hospital (SG), National University Health System (SG)
Openalex Percentile: Top 8%
AI in cancer detection
2.48
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