彭世傑 Shih-Chieh Peng
Shih-Chieh Peng is a distinguished specialist in radiological sciences and medical imaging, with an enduring commitment to advancing the frontiers of smart healthcare research. With a vision to revolutionize modern medicine, Shih-Chieh’s primary mission centers on bridging the critical gap between cutting-edge artificial intelligence technologies and real-world clinical practice, seamlessly integrating sophisticated AI systems into diagnostic workflows to dramatically enhance both precision and operational efficiency in healthcare delivery.
Shih-Chieh’s research core encompasses the innovative application of Machine Learning and Deep Learning methodologies across multi-modal medical imaging platforms, including X-ray radiography, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). His work spans the full spectrum of clinical AI applications—from early-stage disease detection and differential diagnosis to comprehensive prognostic modeling and treatment response prediction. Beyond pioneering model architecture design and algorithmic optimization, Shih-Chieh places paramount emphasis on clinical data integration strategies, advanced feature engineering, radiomics analytics, and Explainable AI. This holistic approach ensures that AI models deliver not only exceptional accuracy and reliability but also critical interpretability and transparency for clinical practitioners, fostering trust and facilitating informed decision-making at the point of care.
In addition to rigorous academic research, Shih-Chieh demonstrates deep commitment to the clinical translation and real-world implementation of AI technologies. With comprehensive expertise across the commercialization lifecycle of medical AI devices, his experience includes clinical data governance, performance validation, CDSS implementation, and regulatory strategy analysis for medical device approval pathways.
Shih-Chieh currently serves as a Research Manager at the Department of Radiology, Cathay General Hospital, where he leads and oversees AI-driven imaging research initiatives, clinical data coordination, and multi-disciplinary project execution. In parallel, he works as an Interdisciplinary Engineer at aetherAI Technology Co., Ltd., contributing to both the Product Technical Support team and the Data Science division, focusing on AI system integration, model deployment, and cross-domain technical problem-solving.
As an active participant in international conferences and global collaborative programs, Shih-Chieh passionately promotes interdisciplinary knowledge exchange across radiology, clinical medicine, and AI engineering. Through this multifaceted approach, he continues to drive meaningful innovation that transforms patient care and advances the field of intelligent medical imaging.
Journal Articles
[1] Chen, J.-R., Hou, K.-Y., Wang, Y.-C., Lin, S.-P., Mo, Y.-H., Peng, S.-C., & Lu, C.-F. (2025). Enhanced Malignancy Prediction of Small Lung Nodules in Different Populations Using Transfer Learning on Low-Dose Computed Tomography. Diagnostics, 15(12), 1460.
[2] Dong-Jun Wu, Kuei-Yuan Hou, Shih-Chieh Peng, Kuen-Han Du, Yuan-Heng Mo, Yen-Shu Kuo (2025). Predictive Analysis of Cardiovascular Calcification Detection Using Machine Learning: A Multimodal Approach Integrating Clinical Data Features and Medical Imaging Calcium Scoring. C J Radiologic Tech, 49(3), 140-147.
[3] Hou, K. Y., Chen, J. R., Wang, Y. C., Chiu, M. H., Lin, S. P., Mo, Y. H., Peng, S. C., & Lu, C. F. (2022). Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography. Cancers, 14(15), 3798.
Conference Abstracts
[1] Shih-Chieh Peng, Yung-Cheng Wang, Sen-Ping Lin, Yuan-Heng Mo, Wen-Chan Tszen, Tzu-Hsiang Wang (2025, June). Enhancing Patient Safety and Clinical Efficiency with AI for Pneumothorax Detection. The 25th Asia-Australasia Conference of Radiological Technologist. The 11th ASEAN Conference of Radiographer and Biological Technologists. The 12th Asia Radiation Therapy symposium. The 33rd Annual Conference of Thai Society of Radiological Technologists. Chiang Mai, Thailand.
[2] Shih-Chieh Peng, Sen-Ping Lin, Yuan-Heng Mo, Kuei-Yuan Hou, Wen-Wei Lu, Chi-Hung Weng, Yung-Cheng Wang (2024, November). Evaluating AI-Assisted Clinical Decision Support for Abnormal Patient Alerts in LDCT Imaging using Aortic AI. The 59th Congress of Korean Radiological Technologists & International Conference. Seoul, South Korea.
[3] Shih-Chieh Peng, Sen-Ping Lin, Yuan-Heng Mo, Kuei-Yuan Hou, Wen-Wei Lu, Chi-Hung Weng, Yung-Cheng Wang (2024, November). Evaluating AI-Assisted Clinical Decision Support for Abnormal Patient Alerts in LDCT Imaging using Aortic AI. The 59th Congress of Korean Radiological Technologists & International Conference. Seoul, South Korea.
[4] Kuei-Yuan Hou, Shih-Chieh Peng, Yung-Cheng Wang (2024, March). Using Reporting system of Abnormal Aortic Diameter to Improve Patient Safety. The 57th Annual Meeting of the Chinese Society of Medical Radiology and the 31st East Asian International Academic Symposium. Taipei, Taiwan.
[5] Kuei-Yuan Hou, Shih-Chieh Peng, Yung-Cheng Wang (2023, March). Using a three-dimensional Automatic Segmentation Model to Measure Aortic Diameter in Chest Low Dose Computed Tomography. The 56th Annual Meeting of the Republic of China Society of Medical Radiology and the International Symposium on Medical Imaging. Kaohsiung, Taiwan.