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Jun Deng, PhD

Professor of Therapeutic Radiology; Associate Director for Physics Research and Education, Therapeutic Radiology

Contact Information

Jun Deng, PhD

Office Location

Mailing Address

  • Therapeutic Radiology

    PO Box 208040

    New Haven, CT, 06520-8040

    United States

Research Summary

Dr. Deng’s research is primarily on two domains: artificial intelligence for precision medicine, and artificial intelligence for precision radiotherapy. In 2013 his group developed CT Gently, the world’s first iPhone App that can be used to estimate organ doses and associated cancer risks from CT and CBCT scans. In 2016 with a NIH R01 grant, Dr. Deng led the team to develop a personal organ dose archive (PODA), a big data approach to developing an early warning system to improve patient safety in radiation therapy. In 2019 funded by NSF, his team has been developing a generalizable data framework toward precision radiotherapy. Recently, Dr. Deng has joined a multi-institutional collaborative effort to develop digital twins for cancer patients, which has been funded by NCI in 2020 and DOE in 2021. In 2021, Dr. Deng has pioneered a new initiative towards individualized radiotherapy with patient engagement, an AI-empowered mobile health project funded by Yale Cancer Center.


Specialized Terms: Big Data; Machine Learning; Artificial Intelligence; Medical Imaging; Cancer Screening, Detection, and Prevention; Risk Stratification; Clinical Decision Support; Precision Medicine; Precision Radiotherapy, Digital Twin, Multiscale Modeling.

Extensive Research Description

Dr. Deng's research has been focused on AI for precision medicine, and AI for precision radiotherapy. Some active projects are listed below:

  1. Early cancer detection via statistical analysis of personal health data;
  2. Artificial intelligence for clinical decision support;
  3. Machine learning with radiation oncology big data;
  4. A generalizable data framework toward precision radiotherapy;
  5. Multiscale digital twin modeling of cancer patients;
  6. AI-empowered mobile health and smart medicine.

Coauthors

Research Interests

Artificial Intelligence; Medical Informatics; Radiation Oncology; Public Health Informatics; Early Detection of Cancer; Machine Learning

Research Images

Selected Publications