The Current and Future State of AI Interpretation of Medical Images | NEJM Skip to main content
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As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

The rapid progress in digitalization and automation have led to an accelerated growth in
healthcare, generating novel models that are creating new channels for rendering treatment …

In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Artificial intelligence (AI) has been developed for echocardiography,–, although it has not yet
been tested with blinding and randomization. Here we designed a blinded, randomized non …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …