Artificial Intelligence for Healthcare

We discuss the current and future impact of artificial intelligence (AI) technologies on healthcare. We consider four hierarchical levels of healthcare data generation and processing of increasing complexity and wider implications. At the imaging scanner and instrument level, AI aims at improving, simplifying, and standardizing data acquisition and preparation. We present examples of systems for AI-driven automatic patient iso-centering before a computed tomography scan, deep learning-based image reconstruction, and creation of optimized and standardized visualizations, for example, automatic rib-unfolding. At the reading and reporting levels, AI focuses on the detection and characterization of abnormalities and on automatic measurements in images. We introduce multiple AI systems for the brain, heart, lung, prostate, and musculoskeletal disease. The third level is exemplified by the integrated nature of the clinical data in a patient-specific manner. The AI algorithms at this level focus on risk prediction and stratification, as opposed to merely detecting, measuring, and quantifying images. An AI-based approach for individualizing radiation dose in lung stereotactic body radiotherapy is discussed. The digital twin is presented as a concept of individualized computational modeling of human physiology. Finally, at the cohort and population analysis levels, the focus of AI shifts from clinical decision-making to operational decisions and process optimization.