Digital Pathology and Artificial Intelligence (AI)

Digital Pathology and Artificial Intelligence (AI) are rapidly transforming laboratory medicine, enabling a shift from traditional microscopy and manual workflows toward data-driven, computationally enhanced diagnostics. Advances in whole-slide imaging, machine learning, foundation models, and generative AI are creating new opportunities for improved diagnostic accuracy, workflow efficiency, quality assurance, and precision medicine. Across pathology and laboratory disciplines, AI-powered tools are supporting image analysis, biomarker discovery, laboratory automation, decision support, and integrated interpretation of multimodal data. As these technologies mature, they are reshaping how laboratory services are delivered, while also raising important considerations regarding validation, governance, ethics, and implementation. This Special Issue explores the latest innovations, challenges, and future directions at the intersection of Digital Pathology, AI, and Laboratory Medicine.

The Special Issue aims to bridge the gap between emerging computational research and practical clinical implementation. While the potential of "pixelomics" and foundation models is vast, the laboratory medicine community faces significant hurdles in standardizing these tools for routine diagnostic use. This issue will bring together cross-disciplinary insights to solve the critical challenges of diagnostic scalability and precision. By demonstrating how AI-driven workflows and whole-slide imaging can mitigate global pathologist shortages and reduce turnaround times, the collection addresses immediate operational pressures. Furthermore, it evaluates machine learning models that enhance biomarker quantification and multi-omics fusion to reduce intra-observer variability. Ultimately, this issue provides a robust framework for validation, ethical governance, and the integration of generative AI within regulated environments, serving as a definitive roadmap for transforming raw image data into actionable clinical intelligence.

The scope of this Special Issue encompasses the end-to-end integration of computational tools within laboratory medicine, focusing on the transition from qualitative microscopy to quantitative, AI-augmented diagnostics. We invite contributors to address themes including the development of foundation models for pathology, the validation of whole-slide imaging workflows, and the application of generative AI in reporting. Specific interest is directed toward papers exploring multi-omics fusion, automated biomarker quantification (such as HER2 or PD-L1), and the establishment of robust governance frameworks for AI medical devices. We are seeking diverse manuscript types to provide a holistic view of the field: original research articles presenting novel algorithmic approaches, comprehensive systematic reviews on clinical utility, and technical reports focusing on laboratory automation and quality assurance. Case studies highlighting the real-world implementation challenges and ethical considerations of AI deployment in regulated clinical environments are also highly encouraged to ensure a pragmatic roadmap for practitioners.

Article types and fees

This Special Issue accepts the following article types, unless otherwise specified in the Special Issue description:

• Biomedical Science in Brief
• Case Report
• Editorial
• Letter to the Editor
• Opinion
• Original Research
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Special Issue accepts the following article types, unless otherwise specified in the Special Issue description:

• Biomedical Science in Brief
• Case Report
• Editorial
• Letter to the Editor
• Opinion
• Original Research
• Review

Keywords: Digital Pathology, Artificial Intelligence, Deep Learning, Machine Learning, Agentic AI