Artificial intelligence in healthcare: a technological perspective - Gallego, Marcos; Berman, Adam; Crispin, Mireia (IE University, 2020)
Artificial intelligence (AI) is one of the key technological innovations shaping our modern world. The growing availability of digital health data, combined with the newest technical advancements in computing and data science, form the perfect environment for data-driven, AI-powered medicine to flourish. Many of the challenges presented by AI arise from its very foundation, from biases to transparency or generalizability. This chapter therefore focuses on presenting the basic ideas behind AI algorithms. In particular, it focuses on the different types of learning (supervised vs. unsupervised, shallow vs. deep), and discusses some of the most popular ways in which AI methods have been applied in healthcare. We then discuss the main technological pitfalls, and summarize them into four key questions on transparency, overfitting, performance and robustness that AI models with translational ambitions should be able to address adequately. Finally, AI has the potential to go beyond simple applications, and become one of the key tools to connect medical research and clinical practice. We suggest that, if the field progresses steadily despite all the complexities, it could become the backbone of a knowledge-generating feedback loop between the two.
Keywords: Artificial intelligence;Digital health;Data;AI algorithms; Artificial intelligence; Digital health; Data; AI algorithms