Artificial intelligence is not replacing radiologists—despite decades of claims that AI would render the profession obsolete. While AI models like CheXNet have demonstrated superior accuracy in detecting pneumonia on chest X-rays compared to panels of board-certified radiologists, and dozens of other AI tools now outperform humans on benchmark tests, real-world outcomes tell a different story. Demand for radiologists remains higher than ever, with record numbers of residency positions offered in 2025 and vacancy rates at historic highs. This resilience stems from several key factors. First, AI models often fail to maintain their performance in actual hospital environments. Many are trained on narrow datasets from single institutions, and their accuracy can drop by up to 20 percentage points when tested on data from other hospitals. Differences in imaging equipment, protocols, and patient populations create real-world challenges that benchmarks rarely capture. Even when models are tested across multiple sites, they still struggle with subtle or atypical cases—such as mild pneumonia or conditions that mimic it—where human radiologists excel due to clinical context and experience. Second, regulatory and insurance barriers limit the adoption of fully autonomous AI systems. The FDA requires autonomous tools to be highly robust, refusing to read any scan that is blurry, poorly aligned, or outside their defined scope. Only a few models, like IDx-DR for diabetic retinopathy, have received clearance for standalone use. Even then, malpractice insurers are hesitant to cover AI-generated diagnoses. Most policies explicitly exclude coverage for autonomous software, making hospitals unwilling to adopt such systems without legal and financial protection. Third, radiologists spend only about 36% of their time interpreting images. The rest is devoted to patient communication, coordinating care with other clinicians, teaching, managing imaging protocols, and reviewing scan orders. If AI takes over the diagnostic portion, radiologists simply shift focus to these other high-value tasks. This means AI enhances rather than replaces their role. Historical precedent supports this pattern. When digital imaging replaced film in the early 2000s, radiologist productivity soared—but instead of layoffs, imaging volume increased by 60% between 2000 and 2008. Faster turnaround times made scans more accessible, leading to more frequent use across a broader range of clinical scenarios. This is Jevons paradox in action: increased efficiency fuels higher demand. Today, even with over 700 FDA-cleared radiology AI tools, most are used in limited, assistive roles. Only a small fraction of radiologists report high success with AI adoption. The gap between benchmark performance and clinical impact remains wide. The lesson is clear: AI in radiology is not about replacement. It’s about augmentation. The most effective use of AI is not as a standalone diagnostician but as a tool that supports human expertise—flagging urgent cases, reducing workload, and improving consistency. Until institutional, legal, and behavioral barriers are overcome, human radiologists will remain central to the diagnostic process. In knowledge-intensive fields where tasks are diverse, stakes are high, and demand is elastic, AI often leads to more human work—not less. Radiology’s experience offers a powerful model for how AI will unfold across other professions: not with sudden displacement, but with gradual integration, reshaping roles rather than eliminating them.