Medical imaging is essential to modern diagnosis because it allows clinicians to examine internal anatomy and, in many modalities, aspects of tissue function without surgery. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine each provide different types of information. The growing volume and complexity of imaging data have encouraged the use of artificial intelligence (AI) to assist with image analysis, prioritization, and quantitative assessment. How AI Supports Image Analysis Many imaging systems use machine-learning methods to identify patterns in pixels or voxels. Deep-learning models, particularly convolutional neural networks and newer architectures, can learn image features from annotated examples. Depending on the task, an algorithm may classify an image, detect a suspicious region, segment an organ or lesion, estimate a measurement, or help prioritize studies for review. Preprocessing may include intensity normalization, artifact reduction, image registration, and quality checks. These steps must be chosen carefully because preprocessing can also remove clinically important information. Applications in Biomedical Engineering In breast imaging, AI may support the identification and characterization of suspicious findings in ultrasound or mammography. In chest imaging, algorithms can help flag patterns that need prompt review. In MRI, automated segmentation can quantify structures and support longitudinal comparison. AI can also assist image reconstruction, reducing noise or improving acquisition efficiency when the method has been validated for the intended scanner and patient group. These systems are best understood as decision-support tools rather than replacements for trained clinicians. Evaluation, Bias, and Patient Safety A high accuracy value alone does not prove clinical usefulness. Evaluation should consider sensitivity, specificity, precision, calibration, and performance across different sites, scanners, age groups, and disease prevalence. Data leakage can produce misleading results if images from the same patient appear in both training and test sets. Models may also fail when imaging protocols differ from those used during development. Privacy protection, secure data handling, explainability where feasible, and post-deployment monitoring are important parts of responsible implementation. The Role of the Biomedical Engineer Biomedical engineers connect imaging physics, computing, clinical needs, and device integration. They help define the clinical problem, understand the acquisition process, prepare and quality-check datasets, choose meaningful metrics, and test how an algorithm behaves in realistic workflows. Collaboration with radiologists, sonographers, medical physicists, data scientists, and patients is essential. A well-designed system should fit clinical practice, communicate uncertainty appropriately, and make its limitations clear. Conclusion AI has the potential to make medical images easier to analyze and to support earlier, more consistent detection of disease. Its value depends on careful validation, representative data, transparent evaluation, and continued clinician oversight. Biomedical engineers are central to integrating algorithms with imaging hardware, clinical workflows, and patient-safety requirements. References for further reading World Health Organization. Ethics and Governance of Artificial Intelligence for Health. WHO, 2021. Litjens, G. et al. “A Survey on Deep Learning in Medical Image Analysis.” Medical Image Analysis, 2017, 42: 60–88. Topol, E. J. “High-performance Medicine: The Convergence of Human and Artificial Intelligence.” Nature Medicine, 2019, 25: 44–56