Deep Learning Market Forecast to Reach $296B by 2031
A new market report projects the global deep learning market will grow at a 35.48% compound annual growth rate from 2026 to 2031, eventually surpassing $296 billion. The growth is attributed to broad AI adoption, rising investment in generative AI, and demand for automation in fields like computer vision.
- The radiology AI market alone is projected to grow from $0.76 billion in 2025 to $2.27 billion by 2030, driven by a global shortage of radiologists and increasing patient volumes. North America currently dominates this market, holding a 39.5% share in 2024. - While hospitals are the largest adopters of AI in radiology, diagnostic imaging centers are the fastest-growing segment. This is fueled by a rising demand for specialized diagnostic services and the adoption of advanced imaging techniques that leverage AI. - The U.S. Food and Drug Administration (FDA) is rapidly approving AI algorithms for medical imaging, with over 100 new tools approved in the first half of 2025 alone, bringing the total to 873. Leading vendors in terms of FDA clearances include GE HealthCare with 115, followed by Siemens Healthineers with 86. - Deep learning is the leading technology within the AI in radiology market, recognized for its high accuracy in detecting subtle abnormalities across modalities like CT, MRI, and X-rays. Computed Tomography (CT) is the modality with the largest share of the radiology AI market. - The American Medical Association (AMA) officially recognized AI-augmented services in the 2026 CPT code set, creating new codes for services where AI assists with image interpretation and data analysis. This follows a 2025 CMS rule that doubled the payment for hospitals performing cardiac CT with AI analysis. - Major equipment manufacturers are deeply involved in the AI space, with GE HealthCare's Edison platform, Siemens' AI-Rad Companion, and Philips' AI-driven CT 3500 being key platforms integrating AI into imaging workflows. Startups like Aidoc, Viz.ai, and HeartFlow are also significant players, particularly in areas like workflow triage and stroke detection. - AI is directly addressing operational challenges in imaging by automating routine tasks, which can help reduce radiologist burnout. Studies have shown that the integration of AI can lead to a 30% reduction in the time required to interpret X-rays. - Reimbursement for AI in imaging is still evolving, with most AI tools not yet receiving separate payment. However, the Centers for Medicare & Medicaid Services (CMS) has proposed a $1,000 payment for AI-powered coronary plaque analysis, signaling growing momentum for reimbursement.
