Researchers at AIM investigated the use of LLMs for patient portal messaging.
Read MoreAIM Researchers build foundation model to discover new cancer imaging biomarkers
Read MoreAIM study investigates if AI can highlight social determinants of health from clinical notes
Read MoreAIM investigators developed AI to track muscle mass for children through young adulthood
Read MoreAIM researchers investigate ChatGPT for its ability to provide cancer treatment recommendations
Read MoreAIM researchers developed AI that can diagnose sarcopenia in head and neck cancer.
Read MoreAIM researchers and ethicists establish standards for informing patients in AI clinical trials.
Read MoreIn Nature Comm, AIM scientists show that AI applied to X-rays can be used as a new biomarker source in cancer.
Read MoreAIM investigators published a clinical evaluation of AI algorithms to screen for extranodal-extension on CT.
Read MoreIn Nature Medicine, AIM and TRACERx investigators show the importance of AI-based body composition.
Read MoreAIM investigators developed an oncology AI Fact Sheet to facilitate the safe translation of AI models into cancer clinics.
Read MoreA recent publication validated a lung cancer prediction model in 14,737 patients from Mass General Brigham.
Read MoreWe developed an AI model that can accurately predict distant metastases after treatment for lung cancer patients.
Read MoreAIM investigators found that clinical trials with AI algorithms showed high variability in quality.
Read MoreIn Lancet Digital Health, we published a clinical validation of deep learning algorithms to target lung cancer tumors.
Read MoreIn this paper we demonstrate that deep learning applied to x-rays can be used as a new biomarker source.
Read MoreIn Cancer Cell, we published our perspective on the impact of AI in Clinical Oncology.
Read MoreIn Nature Comm, we show that deep learning can automatically predict cardiovascular events.
Read MoreWe developed several heart segmentation algorithms that work on gated and non-gated CT scans.
Read MoreAIM investigators describe their view on AI reproducibility and transparency.
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