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Over the past years, the ML subfield of deep learning has gained tremendous popularity, as it has yielded superior results in analyzingunstructured data such as medical images, text data, and audio data. This technique is based on large artificial neural networks (ANNs). ANNs form networks inspired by the biological animal brain, consisting of multiple layers of processing units called neurons. Deep learning methods can detect complex data relationships by automatically compressing data and distilling relevant features in various levels of abstraction. This makes it different from statistical approaches such as regression methods, which require explicitly defining independent variables and making assumptions about their relationship to the outcome variable. Another advantage of deep learning is its ability to continue learning and improve performance with larger datasets. Besides applications in computer vision, which is the ability to interpret image data by an AI system, deep learning has also propelled natural language processing (NLP) forward, which is the capacity of a computer to understand written and spoken human language. We refer to recent, extensive reviews that cover the subfields of deep learning and its applications in medicine. A state-of-the-art example is the detection of diabetic retinopathy from retinal images, for which the IDx-DR deep-learning-based software has been FDA-approved and validated in a clinical setting. Relatedly, deep learning approaches have outperformed trained physicians in breast cancer detection using imaging data, with currently nine applications FDA-approved. Some of the latest AI breakthroughs that ignited the general public’s interest are based on deep learning approaches. These involve generative models that are trained to create new data. Examples include deep fakes, DALL-E (an OpenAI application that creates figures and art based on written descriptions), and most recently, ChatGPT, an AI tool that generates highly realistic written text based on user prompts. Figure S1 displays two AI-generated illustrations of this review’s topic.

Learning strategies

A useful categorization of AI is made on the learning strategy, which defines how an algorithm learns from data. Three different approaches are distinguished: supervised, unsupervised, and reinforcement learning. We provide a conceptual framework to structure AI applications based on learning strategy, learning goal, data modality, and medical domain in Figure 2.