Smart Microscopy, AI, and Robotics

Smart Microscopy, AI, and Robotics: Automated Future Disease Detection addresses the need to combine automated microscopy with artificial intelligence and robotics to improve accuracy, throughput, and safety in disease detection. It surveys methods for image acquisition, processing, segmentation, and interpretation across sample types, including blood smears, tissue biopsies, cytology, and cytopathology, equipping researchers, clinicians, and technologists with actionable knowledge for deploying AI-powered diagnostic pipelines.
The book covers foundational principles and advanced applications, beginning with microscopy and contrast-enhanced imaging, followed by automated microscope-robotics integration, image acquisition and analysis pipelines, and AI methods for disease detection. It then explores applications such as blood disorders, infectious diseases, and cancer diagnostics, including AI-based analysis of neurological and cellular imaging, advanced deep-learning architectures, and foundation models for smart microscopy. System-level considerations address performance metrics, explainability, bias mitigation, ethics, clinical governance, regulation, safety, and human-AI interaction and interoperability. The book also examines AI-enabled drug discovery, therapeutic target identification, smart laboratories, automated diagnostics, and global health impact.
Readers will gain practical guidance on building high-throughput, AI-assisted diagnostic systems that are robust, interpretable, and scalable, with insights into data annotation, model validation, and integrating AI with robotic microscopy in clinical and research settings. The work anticipates developments such as self-calibrating instruments, autonomous laboratory workflows, and digital pathology ecosystems.

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• Integrates AI and robotics for automated microscopic diagnostics
• Presents AI-driven detection of diseases from blood/tibial samples and slides
• Details robotics-enabled slide handling, imaging, and workflow automation
• Addresses ethics, regulation, and human-AI collaboration in diagnostics