Assistant Professor of Radiation Oncology at Wake Forest University School of Medicine, United States
Dr. Yuming Jiang, MD, PhD, is an Assistant Professor in the Department of Radiation Oncology at Wake Forest University School of Medicine, North Carolina, USA. His research and clinical expertise focus on the integration of artificial intelligence and digital pathology to improve cancer prognosis and treatment strategies. With a strong background in oncology and computational medicine, Dr. Jiang has made significant contributions to the understanding of tumor microenvironments and the application of deep learning in radiomics.
Profile
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Education
Dr. Jiang obtained his MD and PhD degrees from prestigious institutions, demonstrating a commitment to both clinical practice and scientific research. He completed his PhD in oncology at a leading university in China, followed by a postdoctoral fellowship at Stanford University from 2018 to 2023. In August 2023, he joined Wake Forest University School of Medicine as an Assistant Professor, where he continues to advance the field of radiation oncology through innovative research and patient-centered care.
Experience
Dr. Jiang has a diverse professional background that spans clinical medicine, academic research, and technological innovation. Before joining Wake Forest University, he worked as a Postdoctoral Research Fellow at Stanford University, where he contributed to groundbreaking studies on digital pathology, cancer immunotherapy, and noninvasive imaging techniques. His expertise in artificial intelligence and machine learning has enabled him to develop predictive models for cancer prognosis, treatment response, and survival outcomes.
Research Interests
Dr. Jiang’s research is centered on the intersection of oncology and artificial intelligence. His key interests include deep learning-based radiomics, tumor microenvironment analysis, and predictive modeling for personalized cancer treatment. He aims to harness computational tools to enhance diagnostic accuracy and therapeutic decision-making in radiation oncology. His work has had a profound impact on the understanding of tumor biology and has paved the way for more effective, individualized treatment strategies.
Awards
Dr. Jiang has been recognized for his contributions to oncology and medical research with several prestigious awards. His research has received accolades from leading medical societies and journals, highlighting his role in advancing cancer diagnostics and treatment methodologies. His innovative work in AI-driven oncology has earned him invitations to speak at international conferences and collaborate with esteemed institutions worldwide.
Publications
Jiang Y, Zhang Z, Wang W, Huang W, et al. “Biology-guided deep learning predicts prognosis and cancer immunotherapy response.” Nature Communications, 2023; 14: 5135. (Cited by 16.6)
Jiang Y, Zhou K, Sun Z, Wang H, et al. “Non-invasive tumor microenvironment evaluation using deep learning radiomics.” Cell Reports Medicine, 2023; 4:101146. (Cited by 14.3)
Jiang Y, Zhang Z, Yuan Q, Wang W, et al. “Predicting peritoneal recurrence from CT images using multi-task deep learning.” Lancet Digital Health, 2022; 4(5): e340-e350. (Cited by 36.6)
Jiang Y, Li R, Li G. “Artificial intelligence for clinical oncology: current status and future outlook.” Science Bulletin, 2023; (23): 00113-5. (Cited by 18.9)
Jiang Y, Liang X, Wang W, Chen C, et al. “Radiographical assessment of tumor stroma and treatment outcomes using deep learning.” Lancet Digital Health, 2021; 3(6): e371-e382. (Cited by 36.6)
Jiang Y, Jin C, Yu H, Wu J, et al. “Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer.” Annals of Surgery, 2021; 274(6): e1153-e1161. (Cited by 13.8)
Jiang Y, Wang H, Wu J, Chen C, et al. “Noninvasive imaging evaluation of tumor immune microenvironment in gastric cancer.” Annals of Oncology, 2020; 31(6): 760-768. (Cited by 32.9)
Conclusion
Dr. Yuming Jiang is at the forefront of integrating artificial intelligence into oncology, bringing innovative solutions to cancer diagnosis and treatment. His expertise in deep learning, radiomics, and tumor microenvironment studies has significantly advanced the field of radiation oncology. With a strong research background and a commitment to improving patient outcomes, Dr. Jiang continues to contribute to the medical community through his pioneering work in AI-driven cancer diagnostics and therapy.