Artificial Intelligence In Healthcare Market Size and Share
Market Overview
| Study Period | 2019 - 2030 |
|---|---|
| Market Size (2025) | USD 39.92 Billion |
| Market Size (2030) | USD 196.91 Billion |
| Growth Rate (2025 - 2030) | 37.60% CAGR |
| Fastest Growing Market | Asia-Pacific |
| Largest Market | North America |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0. |
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Artificial Intelligence In Healthcare Market Analysis by Mordor Intelligence
With spending poised to rise from USD 39.92 billion in 2025 to USD 196.91 billion by 2030, the AI in healthcare industry is tracking a compound annual growth rate (CAGR) of 37.6 %. That growth curve effectively inserts an entirely new digital cost center into hospital finance, forcing chief financial officers to re-cast capital-allocation models that were designed a decade ago for electronic medical records. A notable consequence already surfacing in budget hearings is that AI appropriations are being transferred from innovation sandboxes into baseline infrastructure, a subtle shift that elevates algorithmic tooling to the same priority tier as imaging suites and laboratory analyzers. As that shift occurs, institutional investors are beginning to model AI cash flows not as optional upside but as core to future margin stabilization, a signal that valuation frameworks for publicly traded hospital chains may soon reflect algorithmic productivity assumptions by default.
Key Report Takeaways
- Machine learning retains a 38% market share in 2024, yet the forecast indicates that Generative AI will expand at a 48% CAGR between 2025 and 2030.
- Medical imaging and diagnostics hold 31% of market share in 2024; however, drug-discovery platforms are projected to post a 44% CAGR through 2030.
- Software solutions account for 49.8% of the 2024 market size, yet services are on track for a 41% CAGR.
- Healthcare providers command a 46% market share in 2024, whereas Pharmaceutical and biotechnology companies are forecast to grow at a 40% CAGR.
- North America accounts for 58.9% of global market size in 2024, whereas Asia Pacific is expected to grow with CAGR of 42.5% between 2025 and 2030.
Global Artificial Intelligence In Healthcare Market Trends and Insights
Driver Impact Analysis
| Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Growing need to reduce escalating healthcare costs | +5.2% | North America; Europe | Medium term (2-4 years) |
| Growing AI reimbursement pathways | +3.9% | North America; Europe | Short term (≤ 2 years) |
| Increasing data availability in healthcare | +4.7% | North America; Asia-Pacific | Long term (≥ 4 years) |
| Rapid proliferation of cloud-hosted model marketplaces | +3.5% | Global | Short term (≤ 2 years) |
| Increasing incidence of chronic disease & demand for personalized treatment | +4.2% | Global | Long term (≥ 4 years) |
| Ability of AI to improve patient outcomes | +5.0% | Global | Medium term (2-4 years) |
| Source: Mordor Intelligence | |||
Increasing Data Availability: Unlocking Clinical Insights at Scale
Healthcare’s data-generation curve has entered the petabyte era. Tempus, for example, reports roughly eight million de-identified records and more than 300 petabytes of multi-omic and clinical data, giving it connections to about two-thirds of US academic medical centers. For chief analytics officers, that magnitude of proprietary content transforms data from a by-product into an appreciating asset. One strategic inference is that institutions without comparable data pools may resort to federated-learning partnerships so that algorithms can be trained on distributed datasets without breaching privacy regulations.
Increasing Incidence of Chronic Disease: Precision Diagnostics Transform Care
The clinical burden of chronic conditions is forcing health systems to re-examine traditional episodic models of care. Research from the National Institutes of Health shows that AI-powered retinal imaging can flag neuro-degenerative disorders several years before overt symptoms appear. Such early-warning capability implicitly reorders budget priorities: funds historically earmarked for late-stage interventions are starting to migrate upstream toward screening and risk-stratification programs. If this redeployment trend consolidates, actuarial tables used by payers may require recalibration to reflect lower long-term liabilities.
Ability of AI to Improve Patient Outcomes: Clinical Decision Support Evolves
NYU Grossman School of Medicine reports that its NYUTron model predicts hospital readmissions with 80% accuracy, substantially ahead of legacy logistic-regression tools. Beyond the headline metric, the deeper takeaway is that unstructured clinical narrative—once dismissed as anecdotal—can be instrumented at scale to generate quantifiable outcome improvements [1]Eric Oermann, “NYUTron: A Large Language Model for Predicting Readmissions,” NYU Langone Health, nyulangone.org. Strategic IT roadmaps therefore increasingly prioritize natural-language processing pipelines as a core platform layer rather than an experimental add-on.
Growing Need to Reduce Healthcare Costs: Operational Efficiency Drives Adoption
Provider CFOs increasingly frame AI as a cost-containment instrument rather than a clinical luxury. Although multiple consultancies cite dollar-denominated savings figures, the more revealing insight is that AI projects now routinely clear internal investment-committee hurdles that previously favored building expansions. The shift takes on added significance when considering that administrative overhead often outruns patient-care spending in low-margin hospitals, suggesting that the first wave of AI deployments could materially influence bond-rating discussions during refinancing cycles.
Restraint Impact Analysis
| Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Data privacy and security concerns | -3.8% | Europe; North America | Short term (≤ 2 years) |
| Semiconductor export controls & GPU shortages | -2.9% | Asia-Pacific; North America | Short term (≤ 2 years) |
| Regulatory and ethical hurdles | -3.5% | Europe; North America | Medium term (2-4 years) |
| Bias and lack of generalizability | -2.4% | Global | Long term (≥ 4 years) |
| Source: Mordor Intelligence | |||
Data Privacy and Security Concerns: Regulatory Hurdles Intensify
Europe’s forthcoming AI Act classifies most clinical algorithms as high-risk and requires meticulous dataset documentation (Didier Reynders, “Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence,” European Commission[2]Didier Reynders, “Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence,” European Commission, europa.eu. Compliance directors are therefore lobbying for early investment in automated data-lineage tools that can produce audit-ready provenance reports. Counterintuitively, the upfront compliance outlay is being reframed by some boards as a strategic barrier to entry, since smaller rivals may struggle to fund equivalent controls.
Regulatory and Ethical Hurdles: Compliance Frameworks Evolve
FDA Commissioner Robert Califf’s public acknowledgment that the agency alone cannot police the entire AI lifecycle underscores the importance of multiparty governance. The Health Equity Across the AI Lifecycle (HEAAL) framework, which breaks equity evaluation into five domains, is gaining attention as a de facto benchmark. Organizations that embed such guardrails during development find that subsequent institutional-review-board approvals progress more smoothly, indirectly compressing product-launch timelines.
Segment Analysis
Generative AI Disrupts Traditional Approaches in Technology Segment
Machine learning retains a 38% market share in 2024, yet the forecast indicates that Generative AI will expand at a 48% CAGR between 2025 and 2030. An implication often missed is that transformer models are blurring boundaries between structured and unstructured data, creating cross-modality insights that earlier convolutional architectures could not deliver. For example, HealAI, a specialized large-language model reported to outperform GPT-4 by 59% in clinical tasks, points to a future where domain-specific models may command premium pricing in licensing negotiations. The machine-learning market size is still the largest today, but generative tools are expected to narrow that gap rapidly by 2030.
Note: Segment shares of all individual segments available upon report purchase
Diagnostics Lead While Drug Discovery Accelerates in Application Segment
Medical imaging and diagnostics hold 31% of market share in 2024; however, drug-discovery platforms are projected to post a 44% CAGR through 2030. Even so, AI-assisted drug discovery is scaling faster, with algorithm-generated candidates reporting Phase I success rates as high as 80–90 %, roughly double historic averages [3]Nathan Brown, “AI-Enabled Drug Discovery Performance,” ScienceDirect, sciencedirect.com. That differential is altering pharmaceutical portfolio management: pipeline attrition assumptions are being revised downward, freeing capital for broader therapeutic exploration without increasing total R&D spend.
Growth Outpaces Software Dominance in Offering Segment
Software solutions account for 49.8% of the 2024 market size, yet services are on track for a 41% CAGR. The counter-intuitive takeaway is that as AI toolkits become more user-friendly, the bottleneck shifts from code availability to change management. Many executives report that fewer than a third of pilots reach production owing to cybersecurity vetting and workflow redesign. Consequently, consultancies that specialize in clinical integration increasingly price engagements on a risk-sharing basis, aligning fees with realized efficiency gains rather than with billable hours.
Note: Segment shares of all individual segments available upon report purchase
Providers Lead While Pharma Accelerates in End User Segment
Healthcare providers command a 46% market share in 2024, reinforcing the perception that bedside applications remain the primary economic engine. Pharmaceutical and biotechnology companies, however, are forecast to grow at a 40% CAGR. The strategic nuance is that provider systems, having accumulated large-scale real-world evidence, are now indispensable data partners for life-science firms seeking to validate target biology. That reciprocal dependency is catalyzing joint ventures in which revenue sharing spans both therapy sales and decision-support subscriptions.
Geography Analysis
North America accounts for 58.9% of global market size in 2024, underpinned by clear regulatory pathways and abundant venture funding. The region’s leadership is further illustrated by the FDA’s 882 clearances of AI medical devices [4]Jeff Shuren, “Artificial Intelligence and Machine Learning in Medical Devices,” U.S. Food & Drug Administration, fda.gov. For domestic suppliers, an under-the-radar advantage is that early federal guidance often sets the tone for software-liability jurisprudence, indirectly reducing insurance premiums for compliant vendors.
Asia-Pacific is forecast to deliver the highest regional CAGR at 42.5% between 2025 and 2030. Local executives observe that government-backed digital-health campaigns effectively compress the sales cycle for AI platforms by bundling them into national reimbursement schemes. Markets such as India, where public and private payers co-exist in a hybrid model, are consequently emerging as test beds for scalable, low-cost clinical-decision tools. In 2024 the Asia-Pacific diagnostics market size, for example, was a fraction of North America’s, yet the region’s imaging-AI segment is projected to widen at pace, reflecting pent-up demand.
Europe is carving out a distinct competitive identity by embedding trust frameworks into its commercial doctrine. The European Health Data Space aligns with the AI Act to streamline secondary use of health data while preserving patient consent requirements. For multinational corporations, one strategic inference is that successful European pilots can act as templates for privacy-sensitive deployments in other jurisdictions. Germany’s hospital-funding reforms, which explicitly earmark digital-infrastructure grants, further enhance the region’s attractiveness for AI rollouts that require capital-equipment upgrades.
Competitive Landscape
The vendor ecosystem remains moderately fragmented but is moving toward an alliance model. Technology giants with hyperscale capability, such as IBM and NVIDIA, are partnering with clinical leaders to co-develop reference architectures. A noteworthy illustration is Tempus, whose precision-medicine datasets have attracted collaborations with most top-tier oncology drug makers, emphasizing the data-as-moat hypothesis. Conversely, traditional med-tech incumbents are integrating AI modules into existing imaging platforms, thereby defending installed bases against software-only entrants. Because proprietary data assets confer bargaining leverage, acquisition valuations increasingly reflect long-term access rights to curated datasets rather than immediate revenue flows.
Artificial Intelligence In Healthcare Industry Leaders
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Microsoft Corporation
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IBM Corporation
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Google LLC (Alphabet)
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NVIDIA Corporation
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Siemens Healthineers AG
- *Disclaimer: Major Players sorted in no particular order
Recent Industry Developments
- April 2025: Mount Sinai inaugurated its AI Small Molecule Drug Discovery Center to accelerate therapeutic design using in-house data and predictive models.
- March 2025: IBM broadened its relationship with NVIDIA, introducing content-aware storage for Fusion and making NVIDIA H200 instances available on IBM Cloud to support large-scale healthcare workloads.
- December 2024: California enacted Assembly Bill 3030, requiring provider disclosure when generative AI is used in patient communication, effective January 2025.
- October 2024: Cleveland Clinic and IBM began a joint program focused on AI-assisted discovery of non-opioid pain therapies, signaling a shift toward value-oriented pharmaceutical pipelines.
- June 2024: Tempus obtained FDA 510(k) clearance for Tempus ECG-AF, an algorithm that flags patients at risk of atrial fibrillation.
Research Methodology Framework and Report Scope
Market Definitions and Key Coverage
Our study defines the artificial intelligence in healthcare market as all revenue earned worldwide from software, hardware, and service solutions that apply machine learning, computer vision, natural language processing, or similar techniques to clinical decision support, diagnostics, drug discovery, hospital workflow, patient engagement, and payer analytics within regulated provider, payer, pharmaceutical, and patient settings. We include on-premise and cloud deployments and track vendor income from new sales, licensing, subscription, and related implementation services.
Exclusion: Consumer wellness apps without medical oversight or regulatory clearance are outside scope.
Segmentation Overview
- By Technology
- Machine Learning
- Deep Learning
- Natural Language Processing
- Computer Vision
- Generative AI / Foundation Models
- Reinforcement Learning
- Other Technologies
- By Application
- Medical Imaging & Diagnostics
- Robot-assisted Surgery
- Virtual Nursing Assistants
- Drug Discovery & Clinical-Trial Optimisation
- Precision & Personalised Medicine
- Remote Patient Monitoring & Wearables
- Hospital Workflow & Operations Management
- Fraud, Waste & Abuse Detection
- Mental Health & Chatbots
- Dosage Error Reduction & CDS
- By Offering
- Hardware
- Software
- Services ((Deployment, Integration, Managed)
- By End User
- Healthcare Providers
- Healthcare Payers
- Pharmaceutical & Biotechnology Companies
- Patients / Consumers
- CROs & Research Institutions
- By Geography
- North America
- United States
- Canada
- Mexico
- Europe
- Germany
- United Kingdom
- France
- Italy
- Spain
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- South Korea
- Australia
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Rest of South America
- Middle East
- GCC
- South Africa
- Rest of Middle East
- North America
Detailed Research Methodology and Data Validation
Primary Research
Mordor analysts held structured interviews with chief medical information officers, radiologists, hospital CIOs, payer analytics heads, AI product leaders, and regional regulators across North America, Europe, and Asia Pacific. Their insights on budgets, average selling prices, integration timelines, and regulatory pacing tested and refined assumptions drawn from desk work.
Desk Research
We began with public sources such as US FDA 510(k) AI device clearances, European Commission digital health surveys, WHO Global Health Observatory datasets, OECD healthcare IT spending tables, and customs logs of advanced imaging equipment. Company filings, investor decks, and peer-reviewed journals provided technology cost curves and adoption triggers. Select paid databases, Dow Jones Factiva for deal flow and D&B Hoovers for vendor revenue splits, anchored baseline inputs. The sources named are illustrative; many additional open and paid references were consulted to verify facts and close gaps.
Market-Sizing & Forecasting
A top-down model converts national healthcare spending pools into the AI addressable slice using penetration ratios for imaging workstations, EHR installations, and cloud compute. These are trended with inputs such as annual FDA AI clearances, venture funding, and skilled talent headcount. Supplier roll-ups of reported AI revenue and sampled ASP times volume checks give a bottom-up reasonableness screen. Forecasts to 2030 employ multivariate regression with scenario analysis that ties growth to GPU price decline, approval cadence, and hospital capex cycles. Where disclosures were partial, averaged ASPs from primary interviews bridged the gaps.
Data Validation & Update Cycle
Outputs pass three-layer variance checks, senior analyst review, and a final refresh before publication. Models update annually, with interim revisions triggered by material events such as landmark reimbursement decisions or major regulatory guidance.
Why Our Artificial Intelligence In Healthcare Baseline Commands Trust
Published estimates differ because firms vary scope, constant currency rules, refresh cadence, and optimism levels.
By defining scope clearly and refreshing every year, Mordor reduces hidden adjustments that distort comparisons.
Benchmark comparison
| Market Size | Anonymized source | Primary gap driver |
|---|---|---|
| USD 39.92 B (2025) | Mordor Intelligence | |
| USD 14.92 B (2024) | Global Consultancy A | Excludes implementation services and drug discovery use cases |
| USD 29.01 B (2024) | Industry Tracker B | Uses list prices without regional ASP adjustments |
| USD 26.69 B (2024) | Market Observer C | Updates biennially; omits recent FDA data |
Taken together, these contrasts show that Mordor's regularly refreshed, variable-rich framework delivers a balanced, transparent baseline that decision makers can rely on with confidence.
Key Questions Answered in the Report
What is the projected AI in healthcare market size in 2030?
The industry is expected to reach roughly USD 197 billion by 2030, rising from nearly USD 32.92 billion in 2025.
Which technology segment exhibits the fastest growth?
Generative-AI and foundation-model platforms are forecast to expand at about 48 % CAGR between 2025 and 2030.
Why is Asia-Pacific registering the steepest regional CAGR?
Large-scale digital-health campaigns, growing clinical-data reservoirs and supportive data-sovereignty rules collectively drive growth above 40 % annua
What is the principal barrier to wider AI adoption in healthcare?
Data privacy and security compliance, especially in jurisdictions governed by stringent regulations, remain the most immediate drag on deployment timelines.
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