In parallel with the increasing number and variety of medical tests and the widespread use of electronic health records combined with the growing capabilities and capacity of computers have attention to big data. Processing and extracting meaningful interpretations from such vast and complex data require artificial intelligence (AI) that refers to complex software systems that enable computers to augment and even imitate human intelligence and decision-making. Machine learning (ML) is a subfield of AI that uses algorithms to parse and learn data and then apply this new learning to make predictions and informed recommendations.
In recent years, the effects that the digitalization of healthcare services will have on medicine, especially laboratory medicine as seen in the industry, the economy, and social life. The abundance of health data will lead to a shift from analytical competence in diagnostic tests to the ability to integrate data and simultaneously interpret them within the clinical context. Therefore, computational laboratory medicine units should be established and integrated into resident and undergraduate education curricula. Using the computational approach, the promise of improved medical interpretation will further increase the effectiveness of laboratory diagnostics in the process of intensive dialogue/consultation and clinical decision-making. Medical laboratories may play an active role in the future as a "nerve center of diagnostics" and joining the patient and physician to form a "Diagnostics 4.0" triangle.
As the big data continue to grow in healthcare, the need for implementing AI and ML techniques into laboratory medicine is inevitable. In this new AI-supported era, clinical laboratories will move towards a more specialized role in translational medicine, advanced technology, management of clinical information, and quality control of results generated outside the laboratory. The field of laboratory medicine should consider such a development sooner rather than later.