Diagnostic laboratories are in the midst of a transformation and are somewhat at cross-roads. In the face of decreasing revenues and increasing workloads, there is a rise in demand to increase throughput and efficiency while maintaining or improving quality, particularly in clinical diagnostics. In addition, today’s complex mix of therapies offered to a varied demographic and the shift toward precision medicine implies that oncologists and pathologists must work in concert to target the right patient for the right therapy at the right time.
New tools and technologies such as computational and digital pathology, molecular diagnostics and artificial intelligence (AI) are making their way into advanced clinical diagnostics, providing some unique opportunities to incorporate these tools into the evolving health care landscape. Herein we present a cross journal series with articles that would give the viewer a perspective of the current trends and future prospects of AI primarily in clinical diagnostics.
Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published. The Editors have no competing interests with the submissions which are handled through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.
