High-Definition Medicine

Abstract

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.

Figure 1. High Definition Medicine

A flow diagram depicting high definition medicine. The Personal Baseline of Health (personal baseline) is defined via a variety of health technologies described in High Definition Prevention (icons to left). Health risks are determined through the integration of the level of risk associated with deleterious genetic variants and the level of divergence of an individual’s personal health baseline from individualized optimal health ranges (Baseline Health Risks). Deviations from the personal health baseline over time provide early detection and an opportunity to intervene in pre-disease processes, and are evaluated in the context of Baseline Health Risks (Baseline Deviations). Progress towards amelioration of these disease processes is judged on the basis of the return to the personal health baseline. Upon the onset of disease deep profiling (icons to right) defines the appropriate selection of high precision therapeutics.

Figure 2. Personal Baseline of Health

A diagram depicting individualized interpretation of health parameter measurements. An example generic health parameter is indicated here by the circular icon – where elevated values are assumed to be indicative of health risk. The sub-population distributions of this generic health parameter is depicted on the left, where individuals with similar characteristics are indicated by coloration. The individualized interpretation of this health parameter measured in three disparate individuals is depicted to the right. This generic health parameter is measured with the same numerical value across each of these three individuals (vertical dashed line in graphs). The differing interpretation of this health parameter measurement is depicted on the far right.

Figure 3. High Definition Prevention

A diagram depicting high definition prevention. Continuous or frequent assessment of the determinants of health (DNA in drop icon, cardiogram icon, and activity icon) allows for the early detection and response to deviations in health parameters from the personal baseline, before clinically manifest (pain icon), likely preventing or delaying disease onset. When utilized in combination and interpreted in context of known health risks (left curve), the potential for early detection is magnified further.

Figure 4. Billions of High Definition People

A flow diagram depicting digital twins. Disease parameters of a currently diseased individual are characterized deeply using high definition prevention technologies (left). The disease profile is cross-referenced against records from billions of individuals having been similarly profiled (right). The prior diseased individuals whose disease characteristics are most closely matched to the currently diseased individual are interrogated for treatments utilized and health outcomes (top right). Treatment, in this case treatment 1, is prioritized based on prior positive outcomes seen in similarly afflicted individuals (middle).