Search

This commentary discusses the prevalence and causes of anemia in primary care in the Netherlands and the role of laboratory diagnostics in determining the cause of anemia. There are indications that guidelines in primary care regarding anemia are insufficiently followed; there are also indications that the correct laboratory measurements are requested too limited (under-diagnosis). A possible solution lies in the introduction of reflective testing, in which the laboratory specialist has additional diagnostic laboratory tests performed on the basis of the laboratory results and specific characteristics of the patient. Reflective testing is in contrast to reflex testing; in reflex testing, laboratory measurements are added automatically using a simple flowchart. In the future, Artificial Intelligence solutions could play a role in determining the most optimal laboratory diagnostic strategy for the diagnosis of anemia in primary care.

It is of paramount importance that healthcare professionals can participate in the academic and societal debate surrounding medical AI. To realise this critical-constructive guidance of AI, it is necessary to be able to distinguish between different types of AI, different applications of AI and to paint the different shades of grey in the current black-and-white debate. This article describes and nuances eight misconceptions that currently dominate the public debate surrounding AI in healthcare. By asking ourselves as healthcare professionals 'what specifically defines our line of work?' we must define what aspects of our occupation we want to have AI either carry out or support, and in what way.

In the near future, making a correct medical diagnosis will be increasingly supported by artificial intelligence. The development of algorithms that integrate all data from an individual into the diagnostic process calls for a multidisciplinary approach that includes not only healthcare professionals and patients, but also data scientists. Because of the position of the clinical chemist in the current health care process, this medical specialist is naturally suited to initiate the development of self-learning diagnostic algorithms and to take the lead in the process to take big data to the next level and create value for health care.