Artificial Intelligence and Pathology: from Principles to Practice and Future Applications in Histomorphology and Molecular Profiling
Abstract
The complexity of diagnostic (surgical) pathology has increased substantially over the last
decades with respect to histomorphological and molecular profiling and has steadily expanded
its role in tumor diagnostics and beyond from disease entity identification via prognosis
estimation to precision therapy prediction. It is therefore not surprising that pathology is among
the disciplines in medicine with high expectations in the application of artificial intelligence (AI)
or machine learning approaches given its capabilities to analyse complex data in a quantitative
and standardised manner to further enhance scope and precision of diagnostics. While an
obvious application is the analysis of histological images, recent applications for the analysis
of molecular profiling data from different sources and clinical data support the notion that AI
will support both histopathology and molecular pathology in the future. At the same time,
current literature should not be misunderstood in a way that pathologists will likely be replaced
by AI applications in the foreseeable future. Although AI will likely transform pathology in the
coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain
molecular properties deal with relatively simple diagnostic problems that fall short of the
diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent
literature of AI methods and their applications to pathology, and put the current achievements
and what can be expected in the future in the context of the requirements for research and
routine diagnostics.
Research Areas
