Collaboration between clinicians and vision-language models in radiology report generation.

Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial intelligence (AI)-generated reports. We build a state-of-the-art report generation system for chest radiographs, called Flamingo-CXR, and perform an expert evaluation of AI-generated reports by engaging a panel of board-certified radiologists.

Building machines that learn and think for themselves.

We agree with Lake and colleagues on their list of "key ingredients" for building human-like intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand engineering.

Sparse coding of birdsong and receptive field structure in songbirds.

Auditory neurons can be characterized by a spectro-temporal receptive field, the kernel of a linear filter model describing the neuronal response to a stimulus. With a view to better understanding the tuning properties of these cells, the receptive fields of neurons in the zebra finch auditory fore-brain are compared to a set of artificial kernels generated under the assumption of sparseness; that is, the assumption that in the sensory pathway only a small number of neurons need be highly active at any time. The sparse kernels are calculated by finding a sparse basis for a corpus of zebra-finch songs.