An academic program designed to accelerate AI solutions into clinic practice.
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Novel deep learning system for estimation of biological age from face photographs
Guidelines for studies using large language models
Responding to patient messages using LLMs
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
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.