Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges

Within the last ten years, essential steps have been made to bring artificial intelligence (AI) successfully into the field of pathology. However, most pathologists are still far away from using AI in daily pathology practice. If one leaves the pathology annihilation model, this paper focuses on tasks, which could be solved, and which could be done better by AI, or image-based algorithms, compared to a human expert. In particular, this paper focuses on the needs and demands of surgical pathologists; examples include: Finding small tumour deposits within lymph nodes, detection and grading of cancer, quantification of positive tumour cells in immunohistochemistry, pre-check of Papanicolaoustained gynaecological cytology in cervical cancer screening, text feature extraction, text interpretation for tumour-coding error prevention and AI in the next-generation virtual autopsy. However, in order to make substantial progress in both fields it is important to intensify the cooperation between medical AI experts and pathologists.