The global AI in radiology market was valued at USD 1.55 billion in 2024 and is forecast to grow at a compound annual growth rate (CAGR) of 38.31 % from 2025 through 2034, signalling a robust expansion in demand for advanced diagnostic tools that leverage machine learning, deep learning and computer vision across medical imaging workflows. The escalation in digital health infrastructure, coupled with rising imaging volumes and workflow complexity, is driving market penetration strategies centred on high-performance algorithms and cloud-native deployments. In North America, the substantial healthcare expenditure, well-established radiology practices and favourable reimbursement climates have positioned the region as a frontrunner for adoption of AI-enabled radiology solutions.
The regional manufacturing trends reflect a strong presence of software and hardware providers, including those developing localised analytics, and cross-border supply chains are being optimised to serve the U.S. and Canada markets. At the same time, Europe is witnessing regulatory tightening—most notably under the EU’s medical device regulation (MDR) and AI Act proposals—which is affecting time-to-market for AI in radiology offerings and shaping strategic positioning of vendors. Asia Pacific is emerging as a high-potential region, driven by expanding imaging centres in China and India, increasing digitisation of healthcare, government programmes targeting diagnostic capacity expansion, and manufacturing hubs in Southeast Asia that support rapid deployment of AI-powered radiology systems in emerging markets. In this region, supply-chain resilience and localisation strategies are becoming critical as global players look to address tariff impacts and geopolitical tensions between major economies.
Looking at the core market dynamics, the primary drivers include the surge in imaging utilisation (CT, MRI, X-ray), the growing need for faster diagnosis and workflow automation, and the rising investment in radiology AI start-ups by venture capital. For example, North American imaging centres are under intense pressure to reduce turnaround times and enhance diagnostic accuracy, which supports implementation of AI triage and decision-support systems. On the restraint side, the cost of deployment (software licensing, integration, hardware upgrades), concerns around data privacy, the need for large-scale labelled datasets, and regulatory uncertainty (especially in Europe) are limiting faster uptake and restricting regional adoption.
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In Europe, for instance, compliance with the AI Act and MDR means additional certification and post-market surveillance obligations that slow market entry. In terms of opportunities, vendors are increasingly leveraging cloud-based subscription models and value-chain optimisation to serve smaller imaging centres and clinics in Asia Pacific and Latin America—this opens opportunities to penetrate under-served markets where traditional radiology services are limited. Regional manufacturing trends supporting localised hardware–software bundling and lower-cost deployment are also creating new business models. The rise of bundled imaging suites with embedded AI capabilities offers a chance for differentiation and faster scaling. Regarding trends, we see a clear shift toward multimodal imaging analytics (e.g., integrating CT, MRI, X-ray data), the rise of federated learning to address data-privacy constraints, and strategic alliances between radiology vendors, cloud providers and AI-software firms to enable global roll-outs. In North America and Europe, the collaboration between radiologists, data scientists and AI vendors is becoming a competitive differentiator, and in Asia Pacific the outsourcing of annotation services to regional players is gaining traction.
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