The history of medical imaging spans over a century, beginning with the serendipitous discovery by Dr. Röntgen. It has since undergone two major transformative waves. The First Wave in the early 20
th century utilized X-rays and contrast agents to achieve the two-dimensional (2D) visualization of internal human structures, laying the foundation for diagnostic practice.[
1] The Second Wave followed in the 1970s with the advent of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which enabled the sectionalization and high-contrast digitalization of anatomy, dramatically enhancing diagnostic accuracy by essentially eliminating structural overlap.[
2]
However, the widespread use of high-resolution scanners has led to an exponential growth in imaging data, posing unprecedented challenges to the human capacity for processing and time management. It is this growing gap between the volume of data and human cognitive ability that has given rise to the “Third Wave”—Intelligent Imaging.
The Intelligence Wave: Artificial Intelligence reshapes cognitive paradigms
Artificial Intelligence (AI), particularly Deep Learning, is shifting medical imaging diagnosis from qualitative description to quantitative prediction, representing a fundamental change in cognitive paradigm.
In traditional imaging diagnosis, the focus remains on subjective features such as lesion density and morphology, with the primary task being description and classification (“What is this disease?”). Efficiency is fundamentally constrained by human bandwidth.
In contrast, Intelligent Imaging Analysis shifts the focus to high-dimensional quantitative features. The task type evolves from description to prediction and decision-making (“What will happen next, and which intervention is best?”). AI systems overcome the efficiency bottleneck by enabling real-time processing of massive datasets, and they possess the capability for cross-modal data fusion—integrating imaging data with genetics and pathology to realize systems biology approaches, a significant leap from the single-modality approach of the past.
The core value of AI lies in its ability to recognize complex, non-linear patterns. It can automate repetitive tasks such as lesion detection, segmentation, and quantification, thereby redirecting the radiologist’s focus toward more complex consultations and disease management.
Core applications and future directions
Future research in intelligent medical imaging will primarily concentrate on the following three cutting-edge domains:
Automation and standardization of diagnosis and screening
AI systems can screen for abnormalities in massive image datasets with extremely high sensitivity and specificity, especially in high-throughput screening scenarios like lung nodules, breast cancer, and diabetic retinopathy.[
3] This automation not only boosts diagnostic efficiency but also standardizes diagnosis, minimizing inter-observer variability and subjective errors.
Cross-modal fusion and Radiomics
Accurate intelligence extends beyond the image itself. AI’s power lies in its integrative capacity, merging imaging data, genomic data (Genomics), and clinical pathology data (Pathomics) to construct multi-scale, multi-dimensional models. This fusion will provide powerful decision support for tumor grading, molecular subtyping, and predicting the response to immunotherapy.
Real-time interventional guidance and efficacy assessment
The application of AI is extending into the therapeutic domain. In interventional radiology, AI can provide real-time image registration and surgical path planning, enhancing the precision and safety of procedures.[
4] In radiation oncology, AI accelerates automated target contouring, significantly improving the efficiency and quality of treatment plans, and enables real-time tracking of lesion changes during therapy for early assessment of efficacy.
Future outlook: towards equity and augmentation
The Intelligence Wave is not merely a technological revolution; it is also a revolution in healthcare equity. Through cloud-based AI platforms and remote diagnostic systems, high-quality, precise diagnostic capabilities can transcend geographical barriers and reach regions with scarce medical resources, achieving equitable healthcare.
For the radiologist, AI is not a competitor but an amplifier of capability (Augmented Intelligence). By handling primary, repetitive tasks, AI frees the physician to evolve from an image “observer” into an “Intelligent Disease Manager” and “Data Integrator,” focusing more on complex case consultations, multidisciplinary collaborations, and patient communication.
Journals like Intelligent Medical Imaging are dedicated to fostering the deep integration of AI and clinical practice. We believe that through rigorous scientific inquiry and clinical translation, intelligent imaging is poised to become the core driver of the next generation of precision medicine, bringing unprecedented benefits to human health.
The Author(s) 2026. This article is published with open access at journal.hep.com.cn.
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