Medical digital twins are revolutionizing patient care by creating computerised replicas of individuals, allowing healthcare professionals to simulate and identify the most effective treatments without risking patient health. According to researcher Mikael Benson from the Karolinska Institutet, the future may see each patient possessing their own digital twin.
In traditional healthcare, determining the most suitable treatment often involves trial and error. Patients may undergo numerous treatments, leading to prolonged suffering and increased costs. In critical cases, delays in finding effective therapies can have dire consequences. Digital twins offer a solution by simulating treatments on virtual models that mirror individual patients. If a digital twin responds positively to a particular treatment in the simulation, it is likely that the real patient will experience similar benefits.
Mikael Benson explains, “Most diseases are extremely complex, which is why the same treatment has different effects on different people.” He estimates that approximately half of all prescribed treatments may be ineffective. His research group has successfully tested digital twins on mice with rheumatoid arthritis and patients with Crohn’s disease, finding that simulation outcomes closely aligned with real-world results.
The team recently published a study in the journal Cancer Research, refining methods for the early diagnosis of prostate cancer. Currently, they are preparing to explore how digital twins could assist in preventing cancer in patients suffering from ulcerative colitis. “Serious diseases with costly treatments, such as cancer and inflammatory bowel disease, could become important areas of use in routine healthcare,” Benson stated.
For the digital twin models to function effectively, they require extensive biological data about the patient. This includes information on the activity of both healthy and diseased cells, genetics, symptoms, and results from clinical examinations like X-rays. Researchers employ single-cell analysis to examine thousands of patient cells, gathering detailed data for subsequent analysis. Using machine learning, they identify patterns that reveal how diseases manifest in each individual.
Additionally, understanding how various medications interact at the molecular level is crucial for testing virtual treatments. While data exists for many drugs, it remains incomplete for others.
Digital twins exemplify a growing trend in healthcare, where computer simulations are increasingly relied upon across various fields. These models can be tailored for different purposes. In contrast to Benson‘s mathematical models, some researchers are developing digital twins that visually resemble the patient. Such models could facilitate discussions between healthcare providers and patients, making treatment options more tangible and understandable.
While the advancements in digital twin technology present significant opportunities, Benson cautions against their uncritical adoption. The insights gained could lead to increased stress for patients as they face questions about lifestyle choices, such as smoking and exercise. “The importance of the choices a patient makes becomes clear. This may lead to psychological pressure and even feelings of guilt. We need to be aware of that,” he emphasizes.
Looking to the future, Benson envisions a world where individuals can have their own digital twins from a young age, guiding them through their health journeys. The potential for personalised care through digital twins is vast, marking a significant step towards enhancing patient outcomes in healthcare.
