It is confusing. Everywhere you turn, there is talk of image data, biomarkers, computational models, and self-learning algorithms. At the same time, the medical community talks about personalised precision medicine and holistic approaches. In the development of diseases, environmental influences, social conditions, and personal experiences are given as much importance as genetic factors, the microbiome, or pre-existing comorbidities.
The challenge lies in understanding the correlations and links that one draws using the different procedures. The medical community's real fear of being surpassed or even replaced by intelligent systems is in fact a fear of themselves. They are afraid of the amount of data that they have collected, fed into a system and passed on for further processing to computers that are more powerful than they are.
For some diseases like psychiatric disorders, there is little quantitative data that promises objectivity within a general understanding of a disease. However, a lack of objectively measurable parameters makes it difficult to diagnose and predict the course of individual diseases. One reason is the still insufficient infrastructure for big data, and limitations in precision medicine concepts for some medical disciplines.
What is needed is a multidisciplinary clinical infrastructure that is designed to identify and treat people as patients from an early stage. This would be the foundation for data-based procedures that allow medical staff to use big data and computational models in order to derive predictive analytics and forecast the course of individual cases. Recent studies have shown that results can indeed be achieved with astonishing precision in this way.
Of course, the findings are also of interest to the pharmaceutical industry. The meaning of the term "disease interception" stems from the effort to detect and stop diseases before symptoms arise. Accordingly, Janssen Research & Development calls one of its strategic research and development units the "Disease Interception Accelerator". This unit focuses on developing specific measures aimed at intercepting diseases in at-risk populations and improving the health of individuals and communities. This revolution in medicine has therefore also become a business sector.
What remains to be discussed is how to understand an ethically-responsible use of data, which is intended to depict a human being whose fundamental self-understanding goes beyond quantifiable data and imaging techniques. After all, procedures of this kind intervene not only in disease stages, but in social milieus: What is life like as a data set? How useful is the label "high-risk patient" in terms of personal life plans or job prospects? What are the economic interests in the use of such data? When do prevention and prediction turn into determinism? How will profits be distributed? Do better diagnostic tools produce more diseases? Stigmatisation or destigmatisation? What about data misuse?
We know that more complexity calls for more responsibility. We need to clarify how this responsibility is to be shared, not only in terms of privacy policies, but also in terms of accountability to patients.