Artificial intelligence in digital pathology - time for a reality check
- PMID: 39934323
- DOI: 10.1038/s41571-025-00991-6
Artificial intelligence in digital pathology - time for a reality check
Abstract
The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical specialties. In clinical oncology, the most promising applications include those involving image analysis, such as digital pathology. In this Perspective, we provide a comprehensive examination of the developments in AI in digital pathology between 2019 and 2024. We evaluate the current landscape from the lens of technological innovations, regulatory trends, deployment and implementation, reimbursement and commercial implications. We assess the technological advances that have driven improvements in AI, enabling more robust and scalable solutions for digital pathology. We also examine regulatory developments, in particular those affecting in-house devices and laboratory-developed tests, which are shaping the landscape of AI-based tools in digital pathology. Finally, we discuss the role of reimbursement frameworks and commercial investment in the clinical adoption of AI-based technologies. In this Perspective, we highlight both the progress and challenges in AI-driven digital pathology over the past 5 years, outlining the path forward for its adoption into routine practice in clinical oncology.
© 2025. Springer Nature Limited.
Conflict of interest statement
Competing interests: S. Badve is on the advisory board for Mindpeak; and is also an ad hoc adviser for Agilent, AstraZeneca, Daichii-Sanyo and Roche-Ventana. A.M. is an equity holder in Picture Health, Elucid Bioimaging and Inspirata Inc.; currently he serves on the advisory board of Picture Health; has sponsored research agreements with AstraZeneca, Boehringer-Ingelheim, Bristol Myers-Squibb and Eli-Lilly; has developed technology licensed to Picture Health and Elucid Bioimaging; is involved in two different R01 grants with Inspirata Inc.; and is a member for the Frederick National Laboratory Advisory Committee. A.A., S. Bharadwaj, G.C. and T.P. declare no competing interests.
References
-
- Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022). - PubMed
-
- Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019). - PubMed
-
- van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021). - PubMed
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