期刊
SEMINARS IN CANCER BIOLOGY
卷 84, 期 -, 页码 129-143出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.semcancer.2021.02.011
关键词
Artificial intelligence; Machine learning; Pathology; Molecular pathology; Image analysis
类别
资金
- German Cancer Consortium (DKTK) [01IS18025A, 01IS18037A]
- German Cancer Consortium
- German Centers for Lung Research
- Berlin Institute of Health
- BIFOLD Machine Learning Center
- German Ministry for Education and Research as BIFOLD - Berlin Institute for the Foundations of Learning and Data [01IS18025A, 01IS18037A]
- German Research Foundation (DFG) as Math + : Berlin Mathematics Research Center [01IS18025A, 01IS18037A]
- Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea Government (Artificial Intelligence Graduate School Program, Korea University) [EXC 2046/1]
- NIH [390685689]
- [2019-0-00079]
- [RO1 CA225655]
The application of artificial intelligence and machine learning in pathology has brought new opportunities to the field. However, current studies mainly focus on relatively simple problems and AI is not yet able to replace pathologists. More research and validation are needed for the further application of AI in pathology.
The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology 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 their capabilities to analyze complex data in a quantitative and standardized 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 enhance both histopathology and mo-lecular 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 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.
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