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Jun Deng

Professor of Therapeutic Radiology; Director of Physics Research, Therapeutic Radiology; Associate Director of Medical Physics Residency Program, Therapeutic Radiology

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. In 2022, Dr. Deng received an Amazon research grant to develop a mobile platform for remote monitoring of cancer patients using smart phone.

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

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 modeling 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.


Research Interests

Artificial Intelligence; Medical Informatics; Radiation Oncology; Public Health Informatics; Early Detection of Cancer; Mobile Applications; Health Care; Machine Learning; Big Data; Health Disparate, Minority and Vulnerable Populations

Research Images

Selected Publications