Large initial set up costs have resulted in slow adoption of full digital pathology, and as a consequence the integration of artificial intelligence is also lagging behind other fields of medicine. Here Anna Correas, David Tellez and Diana Rosentul consider the potential costs of this slower adoption of new technology.
While deep learning technologies undeniably present remarkable potential for improving diagnostic accuracy and optimising laboratory processes, the integration of artificial intelligence (AI) within clinical pathology lags significantly behind other medical fields like radiology, which benefited from early adoption of digital imaging. Factors such as the substantial upfront expenses associated with digital pathology systems and AI integration, coupled with insufficient reimbursement structures, remain significant barriers.1,2 But what is the cost of delaying the benefits of these tools?
This article explores the hidden costs of this hesitancy, going beyond financial considerations to encompass clinical and ethical dimensions, and outlines practical strategies to help bridge the gap between innovation and implementation.
The challenge of inter-observer variability
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