Digital Twins, Digital Triplets, and eXplainable AI, in Precision Health

Abstract

Precision health is about preventing, predicting, and treating diseases precisely with the principles of the right care at the right time for the right patient. Precision health is expected to help increase health equity in general. Digital Twins and Digital Triplets can significantly help in meeting the goals of precision health. In this chapter we present digital twins and digital triplets and their roles in realizing precision health. Digital twin is the digital representation of a physical object in the digital space. In the Precision Health context, digital twins are indeed enablers of machine learning and knowledge mining. It is also useful for the in-silico simulation of a person’s phenotype (health states) and genotype (molecular states) to realize evidence-based medicine. Moreover, using probabilistic graph model and neuro-symbolic AI, digital twins will be useful in mitigating physician’s knowledge gaps or decision gaps to achieve value-based care. In contrast to Digital twins, Digital triplet is the semantic intelligence about the object. Digital triplets capture the semantics by placing semantically similar objects close together in the vector embedding space. This semantic intelligence helps cognition and discover hidden and unknown knowledge and their interrelationships to make accurate clinical and medical predictions. We group digital twins in three major categories, namely, Person Phenotype Digital Twin, Person Genotype Digital Twin, and Physicians’ Brain Digital Twin. Person phenotype digital twin relates to all observable properties of a person and a population. Person genotype digital twin helps understand the molecular properties of a person and a population. Physicians brain digital twin is the doctors’ brain with actionable biomedical knowledge in the virtual space.