This proof of concept study examines using a deep learning-based method for the automatic analysis of digital mammograms as a tool to aid in the assessment of neoadjuvant chemotherapy (NACT) treatment response to breast cancer. The authors found that the initial AI performance was able to indicate the potential to aid in clinical decision-making, but in order to continue exploring the potential of AI in predicting responses to NACT for breast cancer, further research must be undertaken. Key points We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making. Article: Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept Authors: Ida Skarping, Måns Larsson & Daniel Förnvik

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a

