2026 Radiation Oncology Leaders Named
Dr. Issam El Naqa, Chair of the Machine Learning Department, has been named a 2026 Fellow of the American Society for Radiation Oncology (ASTRO)
Machine Learning Department
Artificial intelligence and machine learning algorithms have witnessed tremendous growth as powerful data analytics technologies, however, despite the potentials, their role in medicine and oncology has been underwhelming.
In partnership with the research and clinical teams at Moffitt Cancer Center, the Machine Learning Department will leverage the rich oncology data resources to develop new technologies to accelerate cancer discovery and improve clinical care.
The department focuses on machine/deep learning (ML/DL) application in information retrieval and annotation with natural language processing (NLP), outcomes and decision-making research, ML-aided digital pathology, ML-guided translational oncology (drug discovery and repurposing), among other areas.
Mission:
To design, develop, and translate state-of-the-art patient-centered machine and deep learning algorithms for oncology.
Vision:
To transform personalized cancer care and accelerate scientific discovery in cancer research with machine/deep learning
Values:
Patient-centered machine/deep learning for cancer care and research
Unbiased, generalizable, and interpretable machine/deep learning algorithms
Translate machine/deep learning findings into the clinic to improve cancer care and research
Dr. Issam El Naqa Named Moffitt’s 2024 Researcher of the Year
The top award is given to a faculty member who has made outstanding contributions to the understanding of cancer through innovative research and an impact on the care of patients around the globe.
Dr. Issam El Naqa
Latest News
Find the latest news headlines and publications from the Machine Learning Department.
- Dr. Issam El Naqa Named Moffitt’s Researcher of the Year
- Issam El Naqa Inducted into the 2023 Class of the American Institute for Medical and Biological Engineering College of Fellows
- Moffitt Researchers Develop Technology that Allows First Visualization of Radiation Therapy in a Patient in Real Time
- Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy
- An Ionizing Radiation Acoustic Imaging (iRAI) Technique for Real-Time Dosimetric Measurements for FLASH Radiotherapy
- Why we are Building the First Machine Learning Department in Oncology
- Deep Reinforcement Learning for Automated Radiation Adaptation in Lung Cancer
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