Artificial Intelligence in Diagnostics Market Size & Share Analysis - Growth Trends & Forecasts (2025 - 2030)

Global Artificial Intelligence In Diagnostics Market Trends and Insights

Increasing demand for AI tools in medical imaging & workload reduction

Worldwide shortages of radiologists intensify interest in automation. Siemens Healthineers reports that its AI-Rad Companion Chest CT reduces report preparation time by up to 74%, freeing specialists for complex cases ^{[1]Siemens Healthineers, “AI-Rad Companion Chest CT Delivers 74% Time Savings,” Siemens Healthineers, siemens-healthineers.com}. Emergency departments see similar gains: RapidAI’s stroke-triage model delivers 98% sensitivity, accelerating door-to-needle decisions. Imaging networks such as RadNet spent more than USD 54 million on AI acquisitions in 2024 alone to preserve turnaround speed and referral loyalty. 

Administrators also cite reduced burnout and fewer repetitive stress injuries among sonographers after AI-guided scanning protocols. As imaging volumes keep rising faster than staff rosters, the AI in diagnostic imaging market becomes core infrastructure rather than optional add-on.

Government incentives accelerating AI adoption

National policies now combine grants, standards and payment reform. The U.S. HHS AI Strategy earmarks funding for hospital pilots and codifies an HHS AI Council for ongoing governance. In Europe, the EU AI Act classifies diagnostic imaging algorithms as “high-risk,” but offers regulatory sandboxes and harmonized conformity assessment, lowering multi-country launch costs. Legislatures in 34 U.S. states debated over 250 AI-related healthcare bills in 2025, many mandating coverage or setting liability shields for AI-guided diagnosis. In emerging Asian markets, public procurement programs subsidize cloud-hosted inference services so rural clinics can access city-grade image reading without on-site radiologists.

Surge in venture & strategic funding for AI-diagnostics start-ups

Capital flows speed commercialization. Imagen Technologies secured USD 32 million in Series C financing; AZmed raised USD 16.2 million; and ThinkSono attracted GBP 2.1 million to advance ultrasound AI. Corporate investors mirror this activity: GE HealthCare purchased Intelligent Ultrasound’s clinical AI business for USD 53 million to deepen point-of-care offerings ^{[2]GE HealthCare, “NVIDIA Partnership To Deliver Autonomous Imaging,” GE HealthCare, gehealthcare.com}. Aidoc reserved USD 30 million specifically to train foundation models that span multiple imaging organs within a single architecture. Ready access to cash translates into faster regulatory submissions and broader geographic roll-outs, fuelling compound growth in the AI in diagnostic imaging market.

FDA fast-track 510(k) clearances for AI diagnostic devices

Post-2024, the FDA granted breakthrough designation to algorithms such as Cleerly’s coronary artery disease staging platform and icometrix’s tool for amyloid-related imaging abnormality detectioN. January 2025 draft guidance underscores continuous performance monitoring and bias audits, but also outlines a clear pre-submission workflow, cutting uncertainty for innovators. The streamlined route encourages early alignment between developers and reviewers, shrinking average submission-to-clearance timelines and reinforcing investor confidence.

Reluctance among medical practitioners to adopt AI

Clinicians worry about loss of autonomy, liability exposure and algorithmic opacity. A 2024 survey of Polish physicians found that only 32% were fully comfortable delegating image interpretation to AI, despite 68% agreeing it improves throughput ^{[3]Ewelina Kowalewska, “Physicians and AI in Healthcare: Insights From a Mixed-Methods Study in Poland on Adoption and Challenges,” Frontiers in Digital Health, frontiersin.org}. Experienced radiologists voice concerns that atypical cases fall outside training data, requiring nuanced human judgment. Cost adds friction: department chairs identified procurement expense as the primary roadblock even when quality benefits were acknowledged. Finally, job-security fears linger; almost half of surveyed nurses expressed unease about AI replacing certain tasks, though structured training programs significantly improved acceptance rates.

High procurement & lifecycle-maintenance costs

Initial licensing, GPU infrastructure and PACS integration often exceed USD 1 million for multi-site deployments. Annual support contracts can reach 30% of the upfront price as vendors roll out iterative model updates to meet evolving FDA performance expectations. Smaller laboratories struggle to amortize these expenses across limited procedure volumes. Cloud-based subscription models promise relief but raise data-sovereignty and latency issues in markets with fragile connectivity. As standardization improves, cost curves are expected to drop, yet near-term adoption remains skewed toward well-capitalized health systems.

Segment Analysis

By Diagnostic Modality: Imaging Dominates Despite IVD Acceleration

Diagnostic imaging captured 58.12% of the AI in diagnostic imaging market share in 2024, leveraging decades of archived DICOM files that enable supervised deep-learning at scale. Foundation models, pre-trained on millions of X-ray, CT and MRI slices, now deliver organ-agnostic workflows that read multiple pathologies in one pass. Vendors such as GE HealthCare pair these algorithms with edge processors embedded directly in detectors, cutting latency and reducing data-center fees. The AI in diagnostic imaging market size for imaging is projected to expand steadily as autonomous acquisition protocols reduce retakes and standardize quality, making AI indispensable in resource-tight departments.

In vitro diagnostics (IVD) is the fastest-growing modality, set to scale at a 33.75% CAGR through 2030. Pattern-recognition networks classify genomic variants, metabolomic spectra and microbial signatures with higher specificity than rule-based analyzers. The AI in diagnostic imaging market size for IVD will benefit from turnkey cloud APIs that deliver assay interpretation to small labs without dedicated data scientists. Digital pathology sits at the intersection of imaging and IVD; slide scanners feed whole-slide images into convolutional nets that mark tumor margins and grade inflammation, allowing pathologists to focus on complex differential diagnoses.

Note: Segment shares of all individual segments available upon report purchase

By Application: Oncology Disrupts Neurology’s Leadership

Neurology held 25.85% of AI in diagnostic imaging market share in 2024, underpinned by stroke, epilepsy and dementia workloads that demand rapid intervention. Real-time CT perfusion mapping shortens door-to-thrombolysis times, while 3D volumetry quantifies multiple sclerosis lesion load with sub-millimeter precision. Yet oncology is advancing at a 34.16% CAGR to 2030, propelled by tools such as Clairity BREAST, the first device authorized to predict five-year breast-cancer risk from a single mammogram. Multimodal fusion of radiology, pathology and genomic data further elevates accuracy, shifting oncology from image-centric detection to holistic prognostication.

Cardiology continues steady adoption as HeartFlow’s AI-derived plaque analysis gains Medicare coverage, validating reimbursement-driven uptake. Infectious-disease algorithms classify pathogen species directly from chest X-rays or broad-spectrum sequencing, a priority in antimicrobial-resistance stewardship. Meanwhile, obstetric AI tracks fetal growth curves in motion-compensated 3D ultrasound, expanding prenatal care access in low-resource regions.

By End-User: Laboratories Challenge Hospital Dominance

Hospitals commanded 58.45% of the AI in diagnostic imaging market share in 2024 thanks to large scanner fleets, enterprise PACS and in-house IT teams capable of supporting GPU clusters. Integrated AI raises scanner utilization, creating headroom without capital expansion. The AI in diagnostic imaging market size attributable to hospitals will keep growing as reimbursement stabilizes and bundled-payment models reward diagnostic accuracy.

Diagnostic laboratories post the fastest 33.78% CAGR by leveraging AI to automate high-volume studies ranging from full-body CT screenings to multiplex PCR interpretation. The AI in diagnostic imaging market size for laboratories is lifted by remote-read services that route images through secure clouds to subspecialists thousands of miles away. Partnerships such as Integral Diagnostics’ expansion of Aidoc workflows across Australia and New Zealand show how independent chains level the playing field against tertiary hospitals. Ambulatory centers and tele-imaging platforms round out the user base, exploiting software-as-a-service models that eliminate on-site GPU ownership.

Note: Segment shares of all individual segments available upon report purchase

Geography Analysis

North America remains the epicenter, accounting for 54.12% of global revenue in 2024. Early FDA clearances—129 radiology AI devices since 2015—established a precedent that de-risked pilot budgets, while new CMS codes ensure monetization for stroke detection, cardiac CTA analysis and breast-cancer triage. Strategic collaborations are a hallmark: GE HealthCare and NVIDIA co-develop autonomous imaging suites to offset staff shortages, and Siemens Healthineers inks decade-long “value partnerships” bundling scanners, AI software and managed service agreements.

Europe follows closely. The EU AI Act mandates risk-based classification, model transparency and bias testing but also provides common technical documentation templates, accelerating cross-border market entry. Vendors like ThinkSono obtained CE marking for point-of-care ultrasound AI that guides novice operators through DVT scans, broadening the sonographer pool. National health systems in Germany, France and the Nordics run reimbursement pilots that tie AI performance to outcome-based bonuses.

Asia-Pacific is the fastest-growing territory. Government-backed electronic medical-record rollouts in India, Japan and South Korea generate structured image archives ideal for machine-learning. The National Health Authority of India and IIT Kanpur launched open-access datasets under IndiaAI to spur domestic algorithm development. Regional leaders like Qure.ai deployed tuberculosis triage across more than 90 districts, reporting 15 million cumulative patient scans. In China, hospital groups bundle AI lung-nodule detection with annual physicals, illustrating consumer-direct monetization outside insurance frameworks.