Digital Pathology AI Faces a Reality Check: Progress and Barriers to Clinical Adoption
This perspective from Nature Reviews Clinical Oncology critically examines five years of AI development in digital pathology (2019-2024), spanning technological innovation, regulatory frameworks, deployment challenges, and reimbursement models. While acknowledging substantial progress in algorithm performance, the authors highlight that regulatory uncertainty around laboratory-developed tests and in-house devices, along with unclear reimbursement pathways, remain major barriers to routine clinical adoption in oncology.
The original study
Artificial intelligence in digital pathology - time for a reality check.
- Authors
- Aggarwal A, Bharadwaj S, Corredor G, Pathak T, Badve S, Madabhushi A
- Journal
- Nature reviews. Clinical oncology
- Type
- Journal Article, Review
- PMID
- 39934323
Original 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.