• Michael Nagler, Henning Nilius, Grégoire Michielin, Mojgan Masoodi, and Carlo R Largiadèr.
• Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. michael.nagler@insel.ch
• Pol. Arch. Med. Wewn. 2024 Aug 8; 134 (7-8).

AbstractLaboratory tests play a central role in medicine, as they help to make diagnoses, assess prognosis and risk of disease, and monitor therapies, thus contributing to 70% of all medical decisions. This cross‑sectional function offers great potential for technologic and organizational innovation to influence health care as a whole. In recent years, a variety of technologies have emerged and entered the field of medical research, or even medical care. A new generation of biosensors enables laboratory tests to be carried out at the point of care and allows for faster medical decisions. Modern devices allow for patient‑centric blood sampling, which eliminates the need for painful blood draws, patient traveling, and limits the workload of health care professionals. Analytical techniques, such as metabolomics, lipidomics, or proteomics can identify biomarkers extremely sensitively, even down to individual cells. Pharmacogenomics allows for determination of genetic polymorphisms that predict a response to chemotherapeutic agents. Machine‑learning approaches can handle large amounts of multilayered data for diagnostic applications. However, this enormous diagnostic potential is far from being utilized and only very few applications have been implemented in clinical practice. Why is this the case? In this article, we describe the key technologic fields, discuss their medical potential, and list obstacles to their implementation. In addition, we present a methodologic framework to support researchers, clinicians, and authorities in development and implementation of novel diagnostic approaches.

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