Publication Highlights
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
AIM researchers investigate ChatGPT for its ability to provide cancer treatment recommendations
AIM researchers developed AI that can diagnose sarcopenia in head and neck cancer.
AIM researchers and ethicists establish standards for informing patients in AI clinical trials.
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.
AIM investigators developed an oncology AI Fact Sheet to facilitate the safe translation of AI models into cancer clinics.
A recent publication validated a lung cancer prediction model in 14,737 patients from Mass General Brigham.
We developed an AI model that can accurately predict distant metastases after treatment for lung cancer patients.
AIM investigators found that clinical trials with AI algorithms showed high variability in quality.
In Lancet Digital Health, we published a clinical validation of deep learning algorithms to target lung cancer tumors.
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.
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.
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.
In Nature Reviews Clinical Oncology, we highlight how AI is transforming the field of radiation oncology to treat patients more accurately and efficiently.
Science Magazine published our perspective on the application of Artificial intelligence in resource-poor health care settings.
JAMA Network Open published our study about using deep learning to extract prognostic information from chest radiographs
JAMA Oncology published our investigation of deep learning methods for lung tumor segmentation.
Clinical Cancer Research published about deep learning applied to serial imaging to improve outcome predictions.
We used AI to quantify the radiographic phenotype of a tumor to predict IO response.
JNCI published our study about trial design of novel technologies for cancer treatment, including artificial intelligence algorithms.
A review of AI applications in the imaging of several tumor types has been published in CA: A Cancer Journal for clinicians.
Deep learning identifies extranodal extension in head and neck cancer better than radiologists; potential to be used for treatment decisions.
Nature Reviews Cancer published our opinion article on the application of artificial intelligence to image-based tasks in the field of radiology.