Artificial Intelligence in cellular and biomedical imaging: bridging research and clinical decision-making through human-machine collaboration.

Abstract

Artificial intelligence (AI) has become a powerful tool in biomedical imaging, allowing for more sophisticated analysis of cellular and tissue material in research labs and hospitals. This review looks at how AI technologies, particularly deep learning approaches, are being used in radiology, digital pathology, and organoid research. We focus on their strengths in image analysis, disease detection, and making workflows more eBicient. Despite impressive analytical capabilities, the clinical value of these AI systems depends on their function in practice. Factors contributing to this include interpretability to doctors, transparency, and how well they integrate with human expertise. We explore current applications across diBerent imaging modalities and discuss why explainability matters for building the right kind of clinical trust. This review also examines various models of human-AI collaboration that can optimize diagnostic performance. Furthermore, research shows that collaborative approaches consistently outperform humans or AI working alone. This is especially true in complex or ambiguous cases where AI supports rather than replaces clinical expertise. We bring together frameworks for evaluating how well human-AI teams perform, look at the challenges involved in taking laboratory successes into real-world hospitals, and point out persistent barriers. Some of these include diBiculties with workflow integration, risks of automation bias, algorithmic bias, and ethical and regulatory questions that haven't been resolved yet. As medical imaging technology keeps advancing, we'll need to optimize these human-machine partnerships through transparent, interpretable systems that support clinical judgment. This will be crucial for realizing AI's potential to improve patient care and make medicine more personalized.