The potential of personalized medicine remains underutilized, and healthcare systems do not need further specialized professionals but a "reconfiguration" of health professions, according to presentations given this week at the European Health Forum Gastein (EHFG) in Gastein, Austria.
Personalized medicine will dominate in the future, presenter Dr. Angela Brand of the Institute for Public Health Genomics (IPHG) at the University of Maastricht told conference attendees: A paradigm change is occurring in medical research, due to better understanding of genetic and environmental influences on health and new findings on the interactions of these factors. An important first step is "stratified medicine," Brand said.
"Stratification means, for example, that one defines groups of patients that could derive especially big benefits from a certain therapy," Brand said. "For instance, based on certain genetic traits of a tumor, we can now predict very precisely for many types of cancer whether or not the given patient would benefit from chemotherapy. This is a major advance in light of the well-known stress chemotherapy causes."
Also highlighted at the conference is the need to "reconfigure" health professions rather than further specialize. Health disciplines in the healthcare system are becoming increasingly specialized and segmented owing to cost pressures, as well as the introduction of management and market mechanisms. This development is a dead end for the growing problem of concurrent chronic conditions, known as multimorbidity, according to conference presenters.
Specialization can be effective in fighting acute single diseases, but it is becoming less relevant given the increase in multimorbidity, as it forces patients to seek out a separate specialist for each problem, according to presenter Dr. Thomas Plochg from the University of Amsterdam. This causes a shortage of health professionals such as general practitioners who maintain an overview of diagnosis and treatment and can help patients to navigate through the system.
"We need to stop exploiting the existing expert model underpinning healthcare provision, and start exploring the reconfiguration of this model instead," Plochg told conference attendees. "'Multimorbidity' physicians and nurses [are key to] the future."
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
