AI-Powered Hematology Education Platform Development

HemoEdge.ai
AI-Powered, WSI-Based Blood Cell Morphology Training for Laboratory Professionals

Concept Overview
HemoEdge is a digital education platform being developed to improve how blood cell morphology is taught, learned and assessed for laboratory professionals working in haematology and diagnostic laboratory settings. The platform is intended for an international audience and is being designed primarily for biomedical scientists, medical laboratory scientists, medical laboratory technologists, haematology laboratory staff, laboratory trainees, educators and training leads in laboratory science.

Our Approach
HemoEdge is being developed to address these gaps through a web-first digital platform that supports standardised, image-based morphology training for laboratory professionals. The approach combines whole slide images illustrating morphological syndromes and cases, cropped images showing specific abnormal blood cell features, annotation layers to support guided interpretation, structured teaching modules across key morphology topics, case-based learning and quiz-based assessment. Each module will also include audio narration to support guided learning, allowing learners to listen to expert-led explanations while reviewing whole slide images, cropped feature images, annotations and case material. The objective is not simply to display images. The aim is to create a more effective, scalable and competency-focused way to teach blood cell morphology, supporting real learning, repeated practice, revision and measurable development.

AI-Powered Learning Support
HemoEdge will also include AI-powered learning support to make the training experience more interactive and responsive. The AI tutor will be designed as an educational assistant, and will help learners ask questions, clarify morphology features, understand case reasoning and receive guided explanations while working through modules and WSI cases. The AI Tutor is not just a chatbot. It is a RAG-based learning assistant that answers questions by retrieving relevant information from HemoEdge’s own expert-reviewed knowledge base. This knowledge base includes teaching notes, feature library entries, case context, annotations, quiz explanations and relevant laboratory values. The AI Tutor combines this retrieved HemoEdge content with the broader language capabilities of Claude Haiku 4.5 to provide clear, context-aware educational support. This retrieval-based approach helps keep the AI Tutor grounded in curated morphology material, while still benefiting from the reasoning and explanation capabilities of a modern large language model.


Whole Slide Imaging and Digital Slide Format
A key part of the platform is the use of WSIs of peripheral blood films. HemoEdge.ai is planning to use OME-TIFF as the preferred WSI format because of its suitability for digital microscopy workflows, metadata handling, and web-based viewing and integration. We are currently working with NationWide Laboratories, NWL, who have agreed to scan selected peripheral blood smear slides for the project. NWL has confirmed that they can support high-resolution scanning of blood films and has agreed to trial conversion of the scanned outputs into OME-TIFF as part of the initial test batch. The purpose of this scanning work is to produce digitised blood films that can be used for educational viewing, annotation, cropped feature image extraction, quiz development, and integration into the HemoEdge learning platform.

Product Direction
The platform is being designed as a web-first digital morphology training environment for laboratory professionals, with future scope for wider platform expansion. The initial product direction includes syndrome-based teaching modules, feature-based learning using cropped cell images, whole slide image viewing, educational annotations, quiz and assessment functionality, learner progress tracking, AI-supported learning assistance and content administration capability for future growth.

Current Status
HemoEdge.ai has moved from concept development into early platform development. Current progress includes: defined product concept and platform direction, early learner app build initial module and case structure, planned WSI viewer integration, clinical advisory (consultant haematologists) engagement, WSI scanning discussions with NWL, agreement with NWL to trial scanning and OME-TIFF conversion, ongoing refinement of the learning app and admin/content management structure.

The current priority is to continue building the full HemoEdge.ai platform in a controlled and logical way, rather than producing only a limited validation prototype. The platform will be developed gradually, with each stage adding stronger educational, technical, and commercial capability. HemoEdge has the potential to become a strong digital training solution in a specialist area where demand for standardised, scalable and high-quality education continues to grow. The goal is to build a platform that is educationally useful, clinically respected, commercially viable and credible to the laboratory and haematology community.

I am particularly interested in speaking with software engineers, including teams who are willing to discuss a standard quotation for full platform development.