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