Opportunities for artificial intelligence in advancing precision medicine

TL;DR

This paper reviews how machine learning and AI are transforming biomedicine by analyzing large-scale biomedical data, highlighting current applications, future needs, and prerequisites for advancing precision medicine.

Contribution

It provides a comprehensive overview of recent progress, future trends, and essential prerequisites for AI and ML in enabling precision health and personalized therapies.

Findings

Deep learning enables digital image recognition and single cell analysis.

AI facilitates automated disease classification and virtual drug screening.

Big data challenges are being addressed to support precision medicine.

Abstract

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.