content — AIM

Deep Learning can improve Lung Cancer Screening

An automated deep-learning approach based on chest x-rays can improve lung cancer screening.

publicationai medicineSeptember 1, 20202020ai, hugo-aerts, michael-lu, udo-hoffmann, vineet-raghu

Defining autonomy in medical AI

In The Lancet Digital Health, AIM investigators have defined the levels of autonomy in medical AI.

publicationai medicineAugust 31, 20202020hugo-aerts, ai, raymond-mak, danielle-bitterman, oncology

Impact of AI on Radiation Oncology

In Nature Reviews Clinical Oncology, we highlight how AI is transforming the field of radiation oncology to treat patients more accurately and efficiently.

Science publishes our article about AI for Global Health

Science Magazine published our perspective on the application of Artificial intelligence in resource-poor health care settings.

publicationai medicineNovember 1, 20192019ahmed-hosny, hugo-aerts, ai

Deep Learning predicts mortality from x-rays

JAMA Network Open published our study about using deep learning to extract prognostic information from chest radiographs

publicationai medicineJuly 17, 20192019ahmed-hosny, hugo-aerts, ai, michael-lu, udo-hoffmann

Deep learning algorithm can support radiation oncology

JAMA Oncology published our investigation of deep learning methods for lung tumor segmentation.

publicationai medicineApril 2, 20192019hugo-aerts, ai, raymond-mak, oncology

Deep Learning Predicts Cancer Treatment Response

Clinical Cancer Research published about deep learning applied to serial imaging to improve outcome predictions.

AI and Imaging can predict Immunotherapy response

We used AI to quantify the radiographic phenotype of a tumor to predict IO response.

publicationai medicineMarch 1, 20192019hugo-aerts, ai, stefano-trebeschi, oncology

Trial Design for the evaluation of oncology devices

JNCI published our study about trial design of novel technologies for cancer treatment, including artificial intelligence algorithms.

publicationai medicineFebruary 28, 20192019ai, raymond-mak, danielle-bitterman

Artificial Intelligence in Cancer Imaging

A review of AI applications in the imaging of several tumor types has been published in CA: A Cancer Journal for clinicians.

publicationai medicineFebruary 27, 20192019ahmed-hosny, hugo-aerts, raymond-mak, ai, radiation-oncology, udo-hoffmann

Deep Learning for Identification of Extranodal Extension

Deep learning identifies extranodal extension in head and neck cancer better than radiologists; potential to be used for treatment decisions.

publicationai medicineFebruary 13, 20192019benjamin-kann, oncology

Artificial Intelligence in Radiology

Nature Reviews Cancer published our opinion article on the application of artificial intelligence to image-based tasks in the field of radiology.

publicationai medicineAugust 2, 20182018ahmed-hosny, hugo-aerts, ai

Deep Learning for Lung Cancer Prognostication

PLOS Medicine published our study exploring deep learning for predicting overall survival in lung cancer patients.

Data Analysis Strategies in Medical Imaging

Clinical Cancer Research published our article outlining best practices for analyzing medical imaging data using AI.

publicationai medicineJanuary 14, 20182018ahmed-hosny, hugo-aerts, ai

Cancer Research published our computational AI system

We published about our computational system to quantify tumor characteristics on medical imaging.

publicationai medicineNovember 1, 20172017hugo-aerts, ai, joost-van-griethuysen

Defining the Biological Basis of Radiomic Phenotypes

eLife published our study revealing links between radiomic, genomic, and clinical data of lung cancer patients.

publicationai medicineMay 12, 20172017hugo-aerts, ai

Decoding Tumour Phenotype in Cancer Imaging

Nature Communications published our study outlining the extraction of radiomic features from cancer imaging data.

publicationai medicineJuly 10, 20142014hugo-aerts, ai, radiation-oncology