Launched in 2023, the Global Initiative on AI for Health (GI-AI4H) brings together three leading United Nations organizations with distinct but complementary expertise in artificial intelligence (AI). The International Telecommunication Union (ITU) drives global standards and connectivity for AI-enabled technologies. The World Health Organization (WHO) provides authoritative guidance on the safe and ethical use of AI in health systems, grounded in its Global Strategy on Digital Health 2020–2025 (extended to 2027) and its AI Ethics & Governance Guidance (2021). The World Intellectual Property Organization (WIPO) serves as the leading international forum on the intersection of AI and intellectual property (IP). Since 2019, WIPO has worked with its member states and stakeholders to support understanding of IP as a catalyst for AI-driven innovation.
Health innovation increasingly applies AI.
By raising awareness and providing information, ITU, WHO and WIPO are committed to the role of IP in continuing to support breakthrough innovations that improve lives everywhere. A broad range of stakeholders, including national governments, regional organizations, health funders, research institutions, industry and civil society organizations also share an interest in this mission. As AI advances and interest in it grows, global cooperation among relevant stakeholders will be essential. Where AI-enabled health innovation flourishes, patients and populations will benefit.
What is ahead in this publication?
ITU, WHO and WIPO jointly developed this publication under the GI-AI4H. The chapters address key themes emerging in AI-enabled health care: IP considerations, including regulatory pathways; commercialization approaches; and health data governance and standards. While each topic is relevant on its own, the three are also interdependent, in considering the development and deployment of AI-enabled health innovations.
Specifically the publication may help to navigate the many decisions required to turn ideas into impact. Considering some aspects of existing legal and other frameworks, it focuses on how to use IP to protect and promote new or improved health care solutions that use AI to address global health challenges, while remaining commercially viable and globally scalable.
Beyond examining relevant topics, this publication provide a range of company case studies from around the world.
The case studies focus on IP and commercialization strategies for AI-enabled health innovation across medical devices, diagnostics, digital health technologies, and related applications. While case studies span various health innovation contexts including pharmaceutical applications to illustrate diverse IP approaches, this publication does not intend to address pharmaceutical regulatory frameworks, drug manufacturing processes, clinical trial requirements for therapeutic products, or pharmaceutical policy matters.
Who should keep reading?
The publication is meant to inform AI-enabled health innovators across the broader health care ecosystem including those who assist them, such as policymakers and regulators, health system implementers, and investment and partnership decision-makers. Practical guidance on using the IP framework to turn ideas into reality helps to advance both innovation and global health impacts.
Key lessons
A mixed IP modality is universal
Innovators deploying effective IP strategies to protect and promote their AI-enabled innovations, which are AI-powered healthcare technologies, employ multiple forms of IP protection simultaneously. Patents may cover a wide range of inventions, including core technical methods such as federated learning, image guidance, reinforcement learning and knowledge graphs. Trade secrets may protect data sets and operational know-how. Copyright may safeguard software implementation, while strategic open-source contributions may build ecosystem trust. No company presented as a case study relied solely on one particular type of IP protection such as patents – different IP rights often have roles to play.
Regulatory clearances enhance enterprise and transactional value
Regulatory authorization emerges as a decisive value inflection point rather than a downstream compliance exercise. Innovators that progress beyond research validation to secure formal regulatory clearances demonstrated materially stronger acquisition outcomes, partnership depth and market credibility compared to peers operating solely at the research or pilot stage.
Regulatory clearance performs several distinct but complementary functions in the AI-enabled health innovation value chain:
Risk reduction for acquirers and partners: Formal authorization by regulators, such as the United States Food and Drug Administration (FDA), the State Council of China National Medical Products Administration (NMPA), the European Medicines Agency (EMA) or European Union (EU) bodies that issue certificates of Conformité Européenne (CE), provide independent verification of safety, performance and clinical relevance. This substantially reduces technical, clinical and liability risks for strategic buyers and enterprise partners, accelerating diligence timelines and increasing the willingness to transact.
Conversion of technical assets into commercial products: Regulatory approval transforms certain types of AI models from experimental tools into deployable medical products with defined intended uses, labeling and post-market obligations. This transition enables scalable revenue models, reimbursement discussions and global distribution, which are key drivers of valuation alongside IP.
Strategic leverage in transactions: In acquisition contexts, cleared products can be immediately integrated into the acquirer’s portfolio. For companies that desire to enter into partnerships, regulatory status enables deeper, longer-term collaborations with hospitals, governments and multinational device manufacturers.
The case studies in this guide demonstrate that regulatory clearance operates as a multiplier of underlying IP assets. For example, patents and trade secrets establish technical differentiation, but regulatory validation converts those assets into realizable enterprise value. In many jurisdictions, regulatory approval is also a prerequisite for the lawful commercialization, distribution, and deployment of regulated healthcare products. For innovators in AI-enabled health innovation, early and deliberate investment in a regulatory strategy significantly increases the probability of successful exits, durable partnerships and sustainable commercialization, particularly alongside IP portfolio development. While enterprise value is key and critically important, innovation also creates significant value for broader public health. Benefits extend beyond commercial returns.
Geographic diversity opens opportunities
AI-enabled health innovation is not limited to traditional biotech hubs. Successful companies using AI in health innovation operate across Europe, Africa, Western Asia, Southern Asia and the Americas. The approach to IP in low- and middle-income countries, including to support equity and access, involves multiple factors, such as differences in digital infrastructure, data availability and technical capabilities. Different stakeholders may prioritize various aspects, from incentivizing innovation to enabling technology diffusion across markets with varying characteristics and capabilities, including through public-private partnerships.
Partnership models vary by technology class and buyer economics
Partnership structures are not uniform; they diverge systematically based on technology type, buyer incentives and regulatory posture. Drug discovery and biological AI platforms primarily operate in business-to-business research and development (R&D) markets. As a result, these platforms favor long-term, milestone-based research collaborations, often spanning 5 to 15 years and structured around shared discovery pipelines, co-development, option-to-license arrangements and downstream royalty participation. This partnership orientation reflects the underlying economics of pharmaceutical R&D, that is, long timelines for development and market approval procedures, and significant technical and clinical risks.
Medical device-oriented AI companies operate in a fundamentally different environment. Their primary customers are hospitals, health systems and imaging networks, where procurement decisions hinge on regulatory clearance, workflow integration, reimbursement potential and immediate clinical utility. Accordingly, these companies prioritize:
Early regulatory approval (FDA, CE, NMPA) to unlock hospital procurement and enterprise sales
Offering products or services (e.g., software as a service, per study pricing and device-embedded software) rather than bespoke research collaborations to grow faster and more profitably
Commercial partnerships or acquisitions with incumbent medical device manufacturers (e.g., ultrasound, imaging, and picture archiving and communication system vendors) seeking to enhance existing product lines
The future demands collaboration
Looking forward, the rapid advancement of generative AI, multimodal models and federated learning will continue to raise questions relevant to IP frameworks, which has always been the case when new technologies emerge. Policymakers, innovators and regulators must work collaboratively so that IP systems continue to support AI-enabled health innovation. The case studies presented in the following chapters demonstrate that health care using AI requires more than technical innovation alone. It calls for strategic IP management, regulatory foresight, collaborative partnerships and a commitment to ethical, patient-centered innovation. As the field evolves, lessons from early pioneers will inform the next generation of innovators in AI-enabled health care worldwide.