The concept of the digital twin, a real-time digital model of an object or process that incorporates all available data and updates itself as new data becomes available, has proven to be an effective logistics and design problem solver in the engineering and manufacturing industries. Now, its application in medicine could help solve problems of a life-threatening nature. Indeed, within the medical sector, digital twins can provide researchers with crucial information for disease pattern detection, treatment effect simulation, and future research pathway discovery.
Digital twins have heretofore been cost prohibitive in healthcare and life sciences, but the recent surge of new technologies hitting the market have lowered the barrier to entry. However, large-scale research such as this requires the mobilization of massive amounts of de-identified patient data. The onus is on researchers themselves to bolster their systems' capabilities in computation, storage, and AI as well as machine learning in order to make insights actionable— all while upholding strict data and privacy bulwarks.
Using patient-similarity comparisons via digital twins is beneficial for the identification of biological markers for disease, and treatment options for patients with similar metrics can also be compared and tested. There is no other comparably advanced analysis currently available to medical research professionals at this scale or with real-life patients. With this data-first approach and streamlining of clinical research methodology, digital twin technology will be able to make a considerable impact as it makes its way into more and more medical research pursuits.