DIGITAL PATHOLOGY
there is sufficient tumour cell content to successfully analyse the entire sample, the slide can be automatically marked for whole-slide scrape.
Future applications in digital pathology
Although it is impossible to correctly forecast which of a myriad of AI applications will advance the fastest towards mass adoption in the clinic, possibilities include adoption of AI tools for tumour detection, slide quality control, IHC scoring, tumour grading, macrodissection, and novel applications. As well as assisting pathologists with their diagnostic workload for high volume cases, such as for breast and colorectal cancers, AI tools will become available for diagnosing rare diseases, assisting pathologists in clinics which do not have specialists for such diseases. Only a few commercially available tools have been approved in the EU and UK for predicting biomarker status from H&E images in a diagnostic setting, and this novel application has not yet been widely translated to the clinic.18,19 Given advancements in life sciences applications, adoption could increase after more in-depth validation studies are performed.20
AI can ‘see’ and analyse
patterns and visual indicators within patient tissue samples that are either too nuanced to reproducibly report and be included in standardised clinical guidelines, or are in fact invisible to the human eye.20
Using such complex
morphological information, AI algorithms will be able to perform as yet unidentified diagnostic and prognostic analysis and suggest biomarker expression scores from H&E images, reducing the need for additional tests, preserving patient tissue, and creating a new method of evaluating pathology slides.21
A challenge remains in
using this information to create human- interpretable features so they can be properly quantified and used in clinical diagnosis. An alternative realisation of this technology could include routine scanning of H&E slides with an algorithm capable of flagging those that are likely to have a genetic mutation present, something already possible for common aberrations like microsatellite instability.19,20
Another potential novel application involves AI review of patient information and charts and incorporating that information into the analysis of pathology images. With AI tools collecting data from patient history files, information about lifestyle choices, prescription medications, and other clinically relevant information could be used to adjust evaluation and render more accurate and
48 LIS | LIMS VNA OME.TIFF PACS
Clinical Data Systems
Scanners
20+ Image Formats (Brightfield and Fluorescence)
DICOM
HALO Link
3rd Party AI
HALO
Other IMS
Research & Admin Data Systems
AI and IA
Clinical AI HALO®
& HALO AI
Fig 4. HALO AP is compatible with a wide range of brightfield and fluorescent image file formats, including DICOM and OME.TIFF and is compatible with a diverse array of scanners, including Hamamatsu and Leica scanners. Bidirectional connectivity via API enables seamless integration with existing laboratory systems including the Xyall Tissector for automated macrodissection workflows.
precise clinical diagnoses and prognoses. For instance, someone who has a lung cancer biopsy could have their smoking history considered while the presence of anthracotic pigment on the biopsy could be automatically removed because they are a known smoker. This would both improve patient care and provide an additional metric for quality control, enhancing patient safety and treatment outcomes. The future also holds the possibility of algorithms and tools being used together in advanced digital pathology workflows. Slides could be automatically scanned using AI-powered slide quality control tools, have their tumour content and IHC expression analysed, and be triaged based on clinical priority before the pathologist ever receives the case. To realise this future, enterprise digital pathology platforms that centralise AI
algorithms for clinical use must be highly flexible and interoperable, enabling laboratories to adapt and work with their laboratory information system or laboratory information management system (LIS | LIMS) of choice. The digital pathology platform must also be user- friendly, intuitive, and accessible to all skill levels.
In summary, the future AI-assisted pathologist will be enabled to work remotely while expediting diagnostic workflows through automated quantification of biomarkers and grading of malignancies, improved slide quality control, AI-powered tumour quantification, and more. With a diverse array of AI applications that could democratise molecular testing, improve patient chart management, and more, the pathologist will increasingly be at the centre of a cockpit of data, enabling them
The process of digitisation and deploying AI-based automated artefact detection algorithms introduces an opportunity to simultaneously scan each slide for artefacts
FEBRUARY 2025
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