  Machine learning (ML), deep learning (DL), and artificial intelligence (AI)
are of increasing importance in biomedicine. The goal of this work is to show
progress in ML in digital health, to exemplify future needs and trends, and to
identify any essential prerequisites of AI and ML for precision health.
High-throughput technologies are delivering growing volumes of biomedical data,
such as large-scale genome-wide sequencing assays, libraries of medical images,
or drug perturbation screens of healthy, developing, and diseased tissue.
Multi-omics data in biomedicine is deep and complex, offering an opportunity
for data-driven insights and automated disease classification. Learning from
these data will open our understanding and definition of healthy baselines and
disease signatures. State-of-the-art applications of deep neural networks
include digital image recognition, single cell clustering, and virtual drug
screens, demonstrating breadths and power of ML in biomedicine. Significantly,
AI and systems biology have embraced big data challenges and may enable novel
biotechnology-derived therapies to facilitate the implementation of precision
medicine approaches.
