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Every year physicians face an increasing demand of image-based diagnosis frompatients, a problem that can be addressed with recent artificial intelligencemethods. In this context, we survey works in the area of automatic reportgeneration from medical images, with emphasis on methods using deep neuralnetworks, with respect to: (1) Datasets, (2) Architecture Design, (3)Explainability and (4) Evaluation Metrics. Our survey identifies interestingdevelopments, but also remaining challenges. Among them, the current evaluationof generated reports is especially weak, since it mostly relies on traditionalNatural Language Processing (NLP) metrics, which do not accurately capturemedical correctness.

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images