As medical science progresses, it is not hard to see how we will be able to integrate ever more data into our digital twins – bringing together information on an anatomical, biomolecular, behavioral, and genetic level, to generate insights that help improve people’s health and quality of life.
But how far can we and should we take the concept of the digital patient?
Some have raised doubts over whether a digital patient with sufficient fidelity is within the grasp of medical science, pointing to the complexity of the human body. 
Indeed, we should recognize that the human body is infinitely more complex than the most elaborate machine. It consists of sub-systems that interact in manifold ways, and the number of variables that influences your health is potentially endless. Unlike engineered objects, which are more or less identical by design, people are very different from each other too. This puts constraints on what we realistically can expect to predict even with the most advanced models.
Critics have also pointed out that in our endeavors to tailor prevention and treatment ever more closely to the individual patient, the improved outcomes may not always weigh up to the costs. In some cases, they argue, there may be more obvious and cheaper interventions that don’t require much individual data – such as a generic lifestyle program focused on diet and exercise. 
Furthermore, there is a concern that continuous monitoring of health data could lead to over-diagnosis, or even over-treatment.
These reservations serve as a reminder that the digital patient is a tool, and not a goal in itself. The goal remains to improve the quality of patient’s lives.
We need to develop digital models around concrete and manageable clinical challenges – always weighing the investment against the expected benefits, and focusing on the most relevant data.
Medical specialists should be strongly involved in these endeavors, because they have the domain knowledge that is so indispensable in creating valid and useful models. Physicians know how to make sense of noisy and incomplete data; something that will always be hard for computers and AI. The resulting digital models should fit seamlessly into their workflows, with intelligence that adapts to their needs and preferences.