Insights
AI in Healthcare
For a long time, pathology was a microscope-and-glass-slide discipline. Digitisation changed that, and once slides became images, AI followed. Today, AI is increasingly part of how tissue samples and diagnostic results are analysed.
Digital pathology turns physical slides into high-resolution images that can be stored, shared, and analysed by software. AI models trained on these images can highlight regions of interest, help quantify features that are tedious to count by hand, and support pathologists in reaching consistent conclusions. In the lab more broadly, AI helps surface patterns across results that might otherwise take longer to connect.
The value shows up as speed, consistency, and scale. A pathologist reviewing hundreds of samples benefits from a tool that pre-screens and prioritises. Rare or subtle findings, where reference examples are scarce, are an active area of research. The goal throughout is to support expert judgement, not to replace the specialist who signs off the result.
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Like imaging, diagnostic AI is a regulated, validation-heavy field. It needs digitised workflows, integration with lab systems, careful checking against the local population, and clinical governance. It is a specialist, enterprise-grade undertaking rather than a quick add-on, and most providers will adopt validated tools rather than build their own.
Is digital pathology the same as AI pathology? No. Digitisation comes first; AI analysis is built on top of digitised slides.
Does AI make the diagnosis? It supports the pathologist, who remains responsible for the result.
Is this widely deployed yet? It is growing, but adoption depends on digitised workflows and regulatory clearance.
MiraalTech tracks the diagnostics landscape and supports providers with the surrounding software and communication tools. Let's talk about where technology can help your practice today.
Hifza Israr
Business Analyst
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