Global Generative AI in Health Technology Assessment (HTA) Market Size, Share, Trends and Forecasts 2031

Key Findings

• The generative AI in health technology assessment (HTA) market focuses on the application of advanced AI models to support evidence synthesis, economic evaluation, and decision-making in healthcare reimbursement and policy frameworks.

• Increasing complexity of clinical data, real-world evidence, and comparative effectiveness studies is accelerating adoption of AI-driven HTA tools.

• Generative AI enhances efficiency in systematic literature reviews, cost-effectiveness modeling, and value-based healthcare assessments.

• Health authorities and payers are exploring AI-enabled solutions to improve transparency, speed, and consistency in HTA evaluations.

• Pharmaceutical and medtech companies are adopting generative AI to optimize dossier preparation and pricing strategies.

• North America and Europe lead early adoption due to mature HTA systems, while Asia-Pacific is emerging as a high-growth region.

• Integration of real-world data, claims data, and electronic health records strengthens AI-driven HTA outputs.

• Regulatory and ethical considerations remain central to AI deployment in public health decision-making.

• Collaboration between AI vendors, HTA bodies, and life sciences firms is shaping solution development.

• Growing pressure on healthcare budgets is increasing demand for faster and more robust assessment methodologies.

Generative AI in Health Technology Assessment (HTA) Market Size and Forecast

The global generative AI in health technology assessment market was valued at USD 0.42 billion in 2024 and is projected to reach USD 1.96 billion by 2031, growing at a CAGR of 24.7%. Growth is driven by rising adoption of AI-based analytics to manage expanding clinical and economic datasets used in reimbursement decisions. Increasing demand for faster HTA timelines and value-based pricing frameworks is accelerating investment in generative AI platforms.

Life sciences companies are integrating these tools into market access and health economics teams to improve submission quality and reduce development cycles. As healthcare systems face mounting cost pressures, AI-enabled HTA solutions are expected to become integral to policy and reimbursement decision-making.

Market Overview

Generative AI in HTA refers to the use of large language models, generative analytics, and machine learning systems to automate and enhance health technology evaluation processes. These technologies support literature review automation, economic model generation, scenario simulation, and comparative effectiveness analysis. Traditional HTA processes are resource-intensive and time-consuming, often delaying patient access to innovative therapies.

Generative AI improves efficiency by synthesizing evidence across trials, real-world studies, and registries at scale. Stakeholders including payers, HTA agencies, pharmaceutical companies, and consultancies are increasingly adopting AI-driven solutions to support transparent and data-driven decision-making. The market is evolving alongside broader digital transformation initiatives in healthcare policy and reimbursement.

Future Outlook

The future of generative AI in HTA will be shaped by deeper integration with real-world evidence platforms, adaptive economic modeling, and explainable AI frameworks. As trust in AI outputs improves, regulatory bodies may formalize guidance on AI-assisted assessments.

Expansion into emerging markets will be supported by the need for scalable HTA capabilities in resource-constrained systems. AI-driven scenario modeling will enable more dynamic pricing and reimbursement negotiations. Advances in interoperability and data governance will further enhance adoption. Over time, generative AI is expected to redefine how value is assessed and communicated across healthcare systems globally.

Generative AI in Health Technology Assessment (HTA) Market Trends

• Automation of Systematic Literature Reviews and Evidence Synthesis
  Generative AI is increasingly used to automate systematic literature reviews by rapidly screening, summarizing, and categorizing large volumes of clinical and economic studies. This significantly reduces manual workload and shortens HTA timelines for new technologies. AI-driven synthesis improves consistency and minimizes human bias in evidence selection. Integration with real-world evidence sources further strengthens assessment robustness. HTA agencies and consultancies are leveraging these tools to handle rising submission volumes. This trend is transforming evidence generation into a more scalable and repeatable process.

• AI-Driven Health Economic and Cost-Effectiveness Modeling
  Generative AI enables rapid development and iteration of cost-effectiveness and budget impact models used in HTA submissions. These tools can simulate multiple pricing, comparator, and population scenarios efficiently. Automated model generation supports faster response to payer questions and reassessments. AI-assisted sensitivity analysis improves transparency and robustness of economic outcomes. Adoption is increasing among pharma market access teams to improve submission quality. This trend enhances decision speed and strategic flexibility in reimbursement negotiations.

• Integration of Real-World Evidence in HTA Processes
  Generative AI facilitates the integration and interpretation of real-world data from claims, registries, and electronic health records. AI models can identify patterns, outcomes, and utilization trends relevant to HTA decisions. This supports post-launch reassessment and adaptive reimbursement models. HTA bodies are increasingly valuing real-world evidence alongside clinical trial data. AI-driven insights improve relevance and contextualization of assessments. This trend aligns HTA outputs more closely with real-world healthcare delivery.

• Use of Generative AI for HTA Dossier Preparation and Review
  Life sciences companies are using generative AI to draft, validate, and optimize HTA dossiers for multiple jurisdictions. AI tools ensure consistency across clinical, economic, and value narratives. Automated gap analysis helps align submissions with country-specific requirements. This reduces rework and accelerates approval timelines. HTA agencies also explore AI to support internal review processes. The trend improves efficiency across both submitter and evaluator workflows.

• Emergence of Explainable and Transparent AI Frameworks
  Explainability is becoming a critical requirement for AI adoption in HTA due to public and regulatory scrutiny. Vendors are developing transparent models that clearly document assumptions, data sources, and reasoning paths. Explainable AI improves trust among policymakers and payers. It also supports auditability and compliance with governance standards. This trend addresses ethical concerns related to algorithmic bias. Transparency is essential for long-term institutional acceptance of AI in HTA.

• Collaborative Ecosystems Between HTA Bodies, Pharma, and AI Vendors
  Collaboration is increasing between HTA agencies, pharmaceutical companies, and AI technology providers to co-develop practical solutions. Joint pilots and sandbox environments help validate AI tools in real assessment scenarios. These partnerships accelerate learning and reduce resistance to adoption. Shared standards and best practices are emerging from collaborative efforts. This ecosystem-driven approach supports responsible innovation. It is shaping scalable and accepted AI-enabled HTA platforms.

Market Growth Drivers

• Rising Complexity and Volume of HTA Submissions
  HTA agencies are facing a growing volume of submissions with increasingly complex clinical and economic evidence. Traditional manual assessment methods struggle to keep pace with this demand. Generative AI enables scalable processing and synthesis of diverse data sources. It reduces bottlenecks and accelerates evaluation timelines. Faster assessments support timely patient access to innovation. This growing complexity directly drives demand for AI-enabled HTA solutions.

• Pressure on Healthcare Budgets and Value-Based Decision-Making
  Governments and payers are under pressure to allocate limited healthcare resources efficiently. HTA plays a central role in determining value for money of new technologies. Generative AI improves accuracy and speed of cost-effectiveness analysis. Better modeling supports evidence-based pricing and reimbursement decisions. AI tools enable scenario testing under budget constraints. This financial pressure is a key driver for adoption.

• Expansion of Real-World Evidence Requirements
  HTA bodies increasingly require real-world evidence to complement clinical trial data. Managing and analyzing these datasets manually is resource-intensive. Generative AI simplifies integration and interpretation of real-world outcomes. It supports longitudinal analysis and post-launch reassessment. AI-driven insights enhance relevance of HTA decisions. Growing reliance on real-world data accelerates AI adoption.

• Digital Transformation of Health Policy and Reimbursement Systems
  Healthcare systems globally are undergoing digital transformation to improve efficiency and transparency. HTA modernization is part of this shift toward data-driven governance. Generative AI aligns with broader digital health and analytics initiatives. It enables standardized, repeatable, and auditable assessment processes. Digital readiness encourages institutional investment in AI tools. This transformation supports long-term market growth.

• Pharmaceutical Industry Demand for Faster Market Access
  Pharma and medtech companies seek to reduce time-to-reimbursement for innovative products. Delays in HTA can significantly impact commercial performance. Generative AI accelerates dossier development and response to HTA queries. Improved efficiency enhances launch planning and lifecycle management. Companies view AI as a strategic enabler of market access. This demand strongly fuels market expansion.

• Advances in AI Models and Data Interoperability
  Improvements in large language models, data integration, and interoperability enhance AI applicability in HTA. Modern AI systems can process structured and unstructured data effectively. Better performance increases confidence among HTA stakeholders. Interoperable data ecosystems enable broader deployment across regions. Technological maturity reduces implementation barriers. These advances underpin sustained growth in the market.

Challenges in the Market

• Data Quality, Bias, and Standardization Issues
  HTA outcomes depend heavily on data quality and methodological rigor. Inconsistent or biased datasets can lead to unreliable AI outputs. Generative models may amplify existing data gaps if not properly governed. Standardization across sources remains limited in many regions. Ensuring high-quality inputs requires significant oversight. Data reliability remains a core challenge for AI-enabled HTA.

• Regulatory, Ethical, and Governance Concerns
  Use of AI in public decision-making raises ethical and governance questions. HTA bodies require assurance that AI recommendations are transparent and unbiased. Regulatory guidance for AI-assisted HTA is still evolving. Lack of clear frameworks slows institutional adoption. Accountability for AI-driven decisions must be clearly defined. These concerns create cautious adoption behavior.

• Limited Trust and Acceptance Among HTA Stakeholders
  Policymakers and clinicians may be skeptical of AI-generated insights. Concerns exist regarding explainability and loss of expert judgment. Building trust requires extensive validation and education. Early adoption often remains limited to pilot projects. Resistance can slow large-scale deployment. Stakeholder acceptance remains a significant barrier.

• Integration Challenges With Legacy HTA Systems
  Many HTA agencies operate on legacy IT infrastructure. Integrating modern AI platforms with existing workflows can be complex. Interoperability issues increase implementation time and cost. Change management is required to adapt processes and skills. Limited technical capacity may hinder adoption. Integration complexity constrains market growth.

• High Implementation and Operational Costs
  Deploying generative AI solutions requires investment in technology, data infrastructure, and skilled personnel. Smaller HTA bodies and emerging markets may face budget constraints. Ongoing model maintenance and validation add operational costs. Cost-benefit justification is required for adoption decisions. Financial barriers may slow penetration. Cost considerations remain a practical challenge.

• Legal Liability and Accountability Risks
  Use of AI in HTA raises questions about responsibility for decisions influenced by algorithms. Incorrect or biased outputs may have policy and financial consequences. Legal frameworks for AI accountability are still developing. HTA agencies must manage risk exposure carefully. Clear governance structures are required. Liability concerns continue to limit aggressive adoption.

Generative AI in Health Technology Assessment (HTA) Market Segmentation

By Application

• Systematic Literature Review Automation

• Health Economic Modeling

• Real-World Evidence Analysis

• HTA Dossier Preparation

• Policy and Reimbursement Decision Support

By End User

• Health Technology Assessment Agencies

• Pharmaceutical and Biotechnology Companies

• Medical Device Manufacturers

• Market Access and HEOR Consultancies

• Government and Public Health Organizations

By Deployment Mode

• Cloud-Based Platforms

• On-Premise Solutions

• Hybrid Deployment

By Region

• North America

• Europe

• Asia-Pacific

• Latin America

• Middle East & Africa

Leading Key Players

• IQVIA Holdings Inc.

• Clarivate Plc

• SAS Institute Inc.

• Oracle Corporation

• IBM Corporation

• Microsoft Corporation

• OpenText Corporation

• Palantir Technologies Inc.

• Veeva Systems Inc.

• Evidera (PPD, Thermo Fisher Scientific)

Recent Developments

• IQVIA expanded its AI-enabled HEOR analytics platform to support automated HTA evidence synthesis.

• Clarivate introduced generative AI tools to streamline literature review and dossier preparation workflows.

• SAS Institute enhanced health economic modeling capabilities using advanced AI-driven simulation engines.

• IBM partnered with public health bodies to pilot AI-assisted decision support in reimbursement evaluations.

• Veeva Systems integrated AI-driven content intelligence into market access and HTA submission platforms.

This Market Report Will Answer the Following Questions

• What is the projected growth trajectory of the generative AI in HTA market through 2031?

• Which HTA processes are most impacted by generative AI adoption?

• How is real-world evidence influencing AI-enabled assessment models?

• What regulatory and ethical frameworks govern AI use in HTA?

• Who are the leading technology providers and solution vendors?

• How are pharmaceutical companies leveraging AI for market access strategy?

• What challenges limit large-scale deployment across HTA agencies?

• Which regions are expected to adopt AI-enabled HTA solutions fastest?

• How does AI improve transparency and efficiency in reimbursement decisions?

• What future innovations will shape AI-driven health technology assessment?