Overcoming the challenges to implementation of artificial intelligence in pathology
Open accessAbstract
Pathologists worldwide are facing remarkable challenges with increasing workloads and lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole-slide images has the potential of democratizing the access to expert pathology and affordable biomarkers by supporting pathologists in the provision of timely and accurate diagnosis as well as supporting oncologists by directly extracting prognostic and predictive biomarkers from tissue slides. The long-awaited adoption of AI in pathology, however, has not materialized, and the transformation of pathology is happening at a much slower pace than that observed in other fields (eg, radiology). Here, we provide a critical summary of the developments in digital and computational pathology in the last 10 years, outline key hurdles and ways to overcome them, and provide a perspective for AI-supported precision oncology in the future.
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Implementing AI in clinical practice involves substantial initial and ongoing expenses, presenting challenges for funding despite promising long-term cost efficiencies and better patient outcomes.
“Another obstacle is that implementing AI in clinical practice involves substantial initial and ongoing expenses, presenting challenges for funding despite promising long-term cost efficiencies and better patient outcomes.13”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [13]
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Self-citation
- Authors
- Reis-Filho, J. S., & Kather, J. N.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- University of Leeds (GB)
- Corresponding author
- Kather, Jakob Nikolas
- Venue
- JNCI: Journal of the National Cancer Institute
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-03-17
- Volume
- 115
- Issue
- 6
- Pages
- 608-612
- ISSN
- 0027-8874 (ISSN-L)
- 1460-2105
- PMC
- PMC10248832
- OpenAlex
- W4327684001
- Cited by
- 79
- References
- 35
- Citations by year
- 2026: 31 · 2025: 18 · 2024: 21 · 2023: 9
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- JNCI Journal of the National Cancer Institute (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Breast Cancer Research Foundation
- NIH
- NCI (P50 CA247749 01)
- Cancer Center Core (P30-CA008748)
- German Federal Ministry of Health
- Max-Eder-Programme of the German Cancer Aid (#70113864)
- German Federal Ministry of Education and Research (01KD2104C)
- German Academic Exchange Service (57616814)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in pathology, digital pathology, whole slide images, computational pathology, precision oncology, biomarker extraction, prognostic and predictive biomarkers, barriers to adoption, pathologist workload, AI implementation challenges
- MeSH terms
- Artificial Intelligence, Humans, Medical Oncology, Neoplasms, Prognosis, Precision Medicine
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