Frank Hoebers
Research Fellow
Frank Hoebers, MD PhD is a Radiation Oncologist at Maastro, the Netherlands and an assistant professor at the Faculty of Health, Medicine and Life Sciences of Maastricht University, the Netherlands. Currently, he is a visiting research-scholar at AIM Harvard Medical School and Brigham Women’s Hospital.
He received his training in Radiation Oncology at the Netherlands Cancer Institute in Amsterdam, the Netherlands. Dr. Hoebers obtained his PhD from the University of Amsterdam, the Netherlands. He has completed a fellowship in head and neck radiation oncology at the Department of Radiation Oncology at the Princess Margaret Hospital in Toronto, Canada.
As a Radiation Oncologist he is combining a clinical appointment with a research position. His scientific research has been focused on the identification of predictive and prognostic factors in head and neck cancer and he has received several national and international research-grants in this field.
He has a special interest in the clinical applications of artificial intelligence in medicine, which will contribute to the concept of personalized medicine. Through this research he wants to narrow the gap between clinicians and data-scientists in the field of AI, increase the acceptance in the medical community and ultimately improve implementation.
For his current position at AIM, dr. Hoebers has been award a personal research fellowship by the Hanarth Fund on a project entitled “Predicting radiological extranodal extension in oropharyngeal carcinoma patients using AI”.
email: fhoebers@bwh.harvard.edu
linkedin: https://www.linkedin.com/in/frank-hoebers-43608265/
Research Highlights
Novel deep learning system for estimation of biological age from face photographs
Responding to patient messages using LLMs
AIM study highlighted by MGB News and several outlets - ScienceMag, Science Daily and ecancer.
AIM Researchers build foundation model to discover new cancer imaging biomarkers
AIM study investigates if AI can highlight social determinants of health from clinical notes
AIM investigators developed AI to track muscle mass for children through young adulthood
Bloomberg, WBUR, Yahoo and many other major news outlets feature AIM study on ChatGPT
AIM researchers investigate ChatGPT for its ability to provide cancer treatment recommendations
PLOS Medicine’s top 10% cited included AIM study on deep learning for lung cancer prognostication
In Nature Comm, AIM scientists show that AI applied to X-rays can be used as a new biomarker source in cancer.
AIM investigators published a clinical evaluation of AI algorithms to screen for extranodal-extension on CT.
In Nature Medicine, AIM and TRACERx investigators show the importance of AI-based body composition.
A recent publication validated a lung cancer prediction model in 14,737 patients from Mass General Brigham.
The Moning Show at CNN features our new study predicting cardiovascular risk from x-rays.
We developed an AI model that can accurately predict distant metastases after treatment for lung cancer patients.
In Lancet Digital Health, we published a clinical validation of deep learning algorithms to target lung cancer tumors.
The NIH awards the ModelHub platform for reproducible AI research.
Web of Science awards AIM researcher as among the top highest cited scientists worldwide.
Together with the WHO we defined a framework for the global application of AI-based Medical Devices.
In this paper we demonstrate that deep learning applied to x-rays can be used as a new biomarker source.
In Cancer Cell, we published our perspective on the impact of AI in Clinical Oncology.
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks
As Published in Nature Comm, we developed a deep learning pipeline to automatically predict cardiovascular events by quantifying coronary calcium on CT scans.
In Nature Comm, we show that deep learning can automatically predict cardiovascular events.
We developed several heart segmentation algorithms that work on gated and non-gated CT scans.
Scientific American discusses the hype in AI and highlights study from AIM.
MIT Technology Review highlights our article published in Nature about transparent AI.
AIM investigators describe their view on AI reproducibility and transparency.
An automated deep-learning approach based on chest x-rays can improve lung cancer screening.
In The Lancet Digital Health, AIM investigators have defined the levels of autonomy in medical AI.