The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Emerging role of deep learning‐based artificial intelligence in tumor pathology
Open accessAbstract
The development of digital pathology and progression of state‐of‐the‐art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)‐based AI, in tumor pathology. The DL‐based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high‐level decision‐making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI‐based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.
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Self-citationOne label is all you need: Interpretable AI-enhanced histopathology for oncology
Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.006 · Cited by 28
Artificial intelligence in dermatopathology: Updates, strengths, and challenges
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Metadata
- Authors
- Jiang, Y., Yang, M., Wang, S., Li, X., & Sun, Y.
- Venue
- Cancer Communications
- Published
- 2020-04
- Volume
- 40
- Issue
- 4
- Pages
- 154-166
- ISSN
- 1000-467X (ISSN-L)
- 2523-3548
- OpenAlex
- W3015357052
- Cited by
- 438
- References
- 85
- FWCI
- 16.86
- Type
- journal article
- Language
- en
- Open access
- gold
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