Evaluation of AI diagnostic systems for breast ultrasound: comparative analysis with radiologists and the effect of AI assistance
The AI system significantly improved diagnostic efficiency and accuracy, particularly for junior radiologists, highlighting its potential clinical utility in breast ultrasound diagnostics.
Authors
- Arisa Sato
- Yuka Yashima (ORCID: https://orcid.org/0000-0001-9706-7145)
- Tomoyuki Aruga (ORCID: https://orcid.org/0000-0003-3442-8972)
- Kazunori Kubota
- Ukihide Tateishi (ORCID: https://orcid.org/0000-0002-6548-8698)
- Tomoyuki Fujioka (ORCID: https://orcid.org/0000-0002-7141-8901)
- Mayumi Hara
- Emi Yamaga (ORCID: https://orcid.org/0000-0002-5931-6507)
- Jitsuro Tsukada (ORCID: https://orcid.org/0000-0001-5951-504X)
- Leona Katsuta
- Iichiroh Onishi (ORCID: https://orcid.org/0000-0001-9744-0114)
- M. Mori (ORCID: https://orcid.org/0000-0002-6107-4519)
- Sayumi Tsuyuzaki
Institutions
- Tokyo Medical and Dental University (JP)
- Keio University (JP)
- Kanto Central Hospital (JP)
- Nitobe Memorial Nakano General Hospital (JP)
- Dokkyo Medical University Saitama Medical Center (JP)
- Social Insurance Saitama Chuo Hospital (JP)
- The University of Tokyo (JP)
- Dokkyo Medical University (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1007/s11604-025-01809-2
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96