Osaka International Cancer Institute

Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors—Integration of patient background information and images

Kosuke Kita, Takahito Fujimori, Yuki Suzuki et al.

2023 · 21 Zit.

High‐quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X‐ray diagnosis

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka et al.

2024 · 18 Zit.

QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy

Giulio Antonelli, Diogo Libânio, Albert J. de Groof et al.

2024 · 18 Zit.

A Learning Program for Treatment Recommendations by Molecular Tumor Boards and Artificial Intelligence

Kuniko Sunami, Yoichi Naito, Yusuke Saigusa et al.

2023 · 13 Zit.

The Three-Class Annotation Method Improves the AI Detection of Early-Stage Osteosarcoma on Plain Radiographs: A Novel Approach for Rare Cancer Diagnosis

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka et al.

2024 · 7 Zit.

Use of AI in Diagnostic Imaging and Future Prospects

Norikatsu Miyoshi

2025 · 6 Zit.

Utility of Same-Modality, Cross-Domain Transfer Learning for Malignant Bone Tumor Detection on Radiographs: A Multi-Faceted Performance Comparison with a Scratch-Trained Model

Joe Hasei, Ryuichi Nakahara, Yujiro Otsuka et al.

2025 · 1 Zit.

Identification and Evaluation of Safety-Related Processes in CT Workflows for Patient Safety

Minoru Kawamata, Norihito Takimoto, Ayaka Watanabe et al.

2025 · 0 Zit.

Performance of large language models in addressing patient queries on colorectal cancer screening in different languages: An international study across 28 countries

Maida M, Papaefthymiou A, G Z E K E R E S et al.

2025 · 0 Zit.

Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross‐Sectional Study

Xufei Luo, Bingyi Wang, Yule Li et al.

2025 · 0 Zit.