Artificial intelligence in healthcare—the road to precision medicine

Abstract

Precision medicine aims to integrate an individual's unique features from clinical phenotypes and biological information obtained from imaging to laboratory tests and health records, to arrive at a tailored diagnostic or therapeutic solution. The premise that precision medicine will reduce disease-related health and financial burden is theoretically sound, but its realisation in clinical practice is still nascent. In contrast to conventional medicine, developing precision medicine solutions is highly data-intensive and to accelerate this effort there are initiatives to collect vast amounts of clinical and biomedical data. Over the last decade, artificial intelligence (AI), which includes machine learning (ML), has demonstrated unparalleled success in pattern recognition from big data in a range of domains from shopping recommendation to image classification. It is not surprising that ML is being considered as the critical technology that can transform big data from biobanks and electronic health records (EHRs) into clinically applicable precision medicine tools at the bedside. Distillation of high-dimensional data across clinical, biological, patient-generated and environmental domains using ML and translating garnered insights into clinical practice requires not only extant algorithms but also additional development of newer methods and tools. In this review, we provide a broad overview of the prospects and potential for AI in precision medicine and discuss some of the challenges and evolving solutions that are revolutionising healthcare.

Ähnliche Arbeiten