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論文業績

2026

  1. Yamamoto K, Kikuchi T. TotalFM: An Organ-Separated Framework for 3D-CT Vision Foundation Models. arXiv. 2026. arXiv:2601.00260

  2. Fujii H, Kikuchi T, Fujii N, et al. Visualization of Nerve Pathology and Correlation with Clinical Severity in Bell Palsy Using 3D Double-Echo Steady-State with Water Excitation Sequence. AJNR Am J Neuroradiol. 2026;47(1):142-150. doi:10.3174/ajnr.A8919

2025

  1. Hirano Y, Miki S, Yamagishi Y, et al. Assessing accuracy and legitimacy of multimodal large language models on Japan Diagnostic Radiology Board Examination. Jpn J Radiol. 2025;44(1):209-217. doi:10.1007/s11604-025-01861-y

  2. Nakamura Y, Sonoda Y, Yamagishi Y, et al. It is Not Time to Kick Out Radiologists. Asian Bioeth Rev. 2025;17(1):9-15. doi:10.1007/s41649-024-00325-1

  3. Yamagishi Y, Nakamura Y, Kikuchi T, et al. Development of a Large-Scale Dataset of Chest Computed Tomography Reports in Japanese and a High-Performance Finding Classification Model. JMIR Med Inform. 2025;13(3):e71137. doi:10.2196/71137

  4. Yamagishi Y, Hanaoka S, Kikuchi T, et al. Using Segment Anything Model 2 for Zero-Shot 3D Segmentation of Abdominal Organs in Computed Tomography Scans. JMIR AI. 2025;4(5):e72109. doi:10.2196/72109

  5. Yamamoto K, Kikuchi T. Feasibility Study of CLIP-Based Key Slice Selection in CT Images and Performance Enhancement via Lesion- and Organ-Aware Fine-Tuning. Bioengineering. 2025;12(10):1093. doi:10.3390/bioengineering12101093

  6. Kikuchi T, Yamagishi Y, Yamamoto K, et al. Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data. JMIR Preprints. 2025. doi:10.2196/preprints.86365

  7. Kikuchi T, Walston SL, Takita H, et al. Scoping Review of Regulatory Transparency in AI-based Radiology Software: Analysis of PMDA-approved SaMD Products. medRxiv. 2025. doi:10.1101/2025.10.02.25336333

2024

  1. Hirano Y, Hanaoka S, Nakao T, et al. No improvement found with GPT-4o: results of additional experiments in the Japan Diagnostic Radiology Board Examination. Jpn J Radiol. 2024;42(11):1352-1353. doi:10.1007/s11604-024-01622-3

  2. Hirano Y, Hanaoka S, Nakao T, et al. GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination. Jpn J Radiol. 2024;42(8):918-926. doi:10.1007/s11604-024-01561-z

  3. Watanabe Y, Matsuki M, Nakamata A, et al. Unveiling the mille-feuille sign: a key to diagnosing ovarian carcinosarcoma in addition to ovarian metastasis from colorectal carcinoma on MRI. Abdom Radiol. 2024;49(7):2499-2512. doi:10.1007/s00261-024-04395-5

  4. Nakamata A, Matsuki M, Otake Y, et al. Magnetic resonance imaging features of complete androgen insensitivity syndrome in comparison to Mayer-Rokitansky-Küster-Hauser syndrome. Abdom Radiol. 2024;49(9):3220-3231. doi:10.1007/s00261-024-04282-z

  5. Kikuchi T, Nakao T, Nakamura Y, et al. Toward Improved Radiologic Diagnostics: Investigating the Utility and Limitations of GPT-3.5 Turbo and GPT-4 with Quiz Cases. AJNR Am J Neuroradiol. 2024. doi:10.3174/ajnr.A8332

  6. Shibata H, Hanaoka S, Nakao T, et al. Practical Medical Image Generation with Provable Privacy Protection Based on Denoising Diffusion Probabilistic Models for High-Resolution Volumetric Images. Appl Sci. 2024;14(8):3489. doi:10.3390/app14083489

  7. Kikuchi T, Hanaoka S, Nakao T, et al. Impact of CT-determined low kidney volume on renal function decline: a propensity score-matched analysis. Insights Imaging. 2024;15:102. doi:10.1186/s13244-024-01671-2

  8. Yamazaki M, Takayama T, Sugihara T, et al. Factors affecting the use of a three-dimensional model during robot-assisted partial nephrectomy. Asian J Endosc Surg. 2024;17(2):e13301. doi:10.1111/ases.13301

  9. Nakao T, Miki S, Nakamura Y, et al. Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study. JMIR Med Educ. 2024;10:e54393. doi:10.2196/54393

  10. Kikuchi T, Hanaoka S, Nakao T, et al. Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases. J Imaging Inform Med. 2024;37(3):1217-1227. doi:10.1007/s10278-024-01015-y

  11. Alam MA, Hanaoka S, Nomura Y, et al. Improved identification of tumors in 18F-FDG-PET examination by normalizing the standard uptake in the liver based on blood test data. Int J Comput Assist Radiol Surg. 2024;19(3):581-590. doi:10.1007/s11548-023-03044-4