Latest Edition of Global AI Competitiveness Index Launched by Deep Knowledge Group Alongside Distinguished Index Committee Members

On 11 May 2026 Deep Knowledge Group launched Global AI Competitiveness Index Part 6: Analyzing AI from a BioTech, Healthcare and Longevity Perspective, alongside a dedicated AI in Biotech, Healthcare and Longevity in Hong Kong report and ecosystem platform. Together, the two releases extend DKG’s AI competitiveness programme into one of the most strategically important applied domains of artificial intelligence: the intersection of biotechnology, healthcare, preventive medicine and longevity innovation.

The Global AI Competitiveness Index is an ongoing benchmarking series developed by the AI Index Consortium, with Deep Knowledge Group serving as the primary provider of underlying analytics. The Index evaluates how countries and city-level hubs build, govern, commercialize and deploy artificial intelligence across major areas of economic and institutional importance. Rather than treating AI competitiveness as a single headline ranking, the series examines the different layers through which AI becomes nationally strategic, commercially scalable and operationally useful:

• Part 1: Enterprise Perspective — mapping AI competitiveness through companies, industrial activity and enterprise ecosystem strength.

• Part 2: Research Innovation Perspective — examining scientific output, patents, academic capacity and research-driven innovation.

• Part 3: Human Capital Perspective — assessing education pipelines, workforce readiness, talent density and human-capital competitiveness.

• Part 4: Policy, Governance and Regulation Perspective — benchmarking how jurisdictions translate AI ambition into governance capacity, regulatory infrastructure and institutional readiness.

• Part 5: Finance, Economy and Financial Services Perspective — analyzing how AI is becoming embedded into financial systems, capital markets, risk infrastructure and economic competitiveness.

• Part 6: BioTech, Healthcare and Longevity Perspective — extending the benchmarking framework into life sciences, where AI competitiveness depends on clinical validation, healthcare data infrastructure, regulatory maturity, capital ecosystems and the ability to scale trusted biomedical solutions.

The launch of the newest edition of the index was marked by a virtual press conference bringing together members of the Global AI Competitiveness Index Committee, including Dame Jenny Shipley, Former Prime Minister of New Zealand; Dr King Au, Former Executive Director of the Hong Kong Financial Services Development Council; Dmitry Kaminskiy, General Partner of Deep Knowledge Group; and Prof. Dr. Patrick Glauner, Professor of Artificial Intelligence at the Deggendorf Institute of Technology.

“What a pleasure it is, as both a former Prime Minister and a former Health Minister, to have high-quality material like this for public servants all around the world. For health ministers trying to decide when and how to deploy AI in systems and across systems, this AI competitiveness report will be invaluable... This is not just part of the global ‘AI everything’ narrative. In healthcare, longevity and applied AI, the question is much more practical: what is already deployable, what is best practice, and how can governments adopt it responsibly?”

— Dame Jenny Shipley, Former Prime Minister of New Zealand; Member of the Global AI Competitiveness Index Committee

The central message of the launch was clear: AI competitiveness in life sciences is no longer determined by research activity alone. It is increasingly determined by deployment-readiness — the ability to validate, govern, finance and integrate AI into real biomedical and healthcare systems.

https://www.youtube.com/watch?v=yWyuftk7NH0

A New Edition of the Global AI Competitiveness Index

The Global AI Competitiveness Index Part 6 benchmarks 20 countries and 20 city-level innovation hubs using a pillar-based framework focused on deployable biomedical AI capability. The report evaluates ecosystems not only by technical strength, but by the practical conditions needed to turn AI into trusted, scalable applications across research, clinics, healthcare systems, diagnostics, capital markets and longevity innovation.

Top country and city-hub scores from AI-GCI Part 6, used in the press-release materials for the launch.

At the country level, the United States ranks first, followed by the United Kingdom, China, Switzerland and Germany. Singapore ranks sixth and Japan ranks tenth. At city-hub level, Boston and San Francisco lead the ranking, followed by Hong Kong, London and New York City. Basel, Zurich, Singapore and Beijing also rank among the leading hubs, while Abu Dhabi places seventeenth as one of the newest rising systems in the benchmark.

The report defines the newest edition not as a generic AI ranking, but as a biomedical deployment-readiness benchmark. It distinguishes between jurisdictions that are strong in AI research and those that can translate AI into regulated, clinically credible and commercially viable systems in biotechnology, healthcare and longevity.

“The top-ranking nations and city-hubs in this edition of the Index are not simply AI-strong; they are strong at turning AI capacity into deployable biomedical systems.”

— Dmitry Kaminskiy, General Partner, Deep Knowledge Group

From General AI Competitiveness to Sector-Specific Deployment

The launch of Part 6 builds on the wider Global AI Competitiveness Index series, which has developed through successive thematic editions examining enterprise AI activity, scientific and technological AI capacity, governance and regulation, and AI in finance. Part 6 applies the same logic to life sciences: in biotechnology, healthcare and longevity, AI must move beyond promising models and research pilots. It must be embedded into clinical workflows, translational infrastructure, regulatory pathways and investment systems.

Global AI in BioTech, Healthcare and Longevity infrastructure landscape used in the broader analytical programme supporting AI-GCI Part 6.

This progression reflects a deliberate analytical logic. The Index series does not treat AI competitiveness as a single headline metric. It examines the layers through which AI becomes economically and institutionally meaningful in different sectors.

Mapped ecosystem snapshot for AI in BioTech, Healthcare and Longevity, showing the breadth of organizations and categories included in DKG’s analytical landscape.

Why BioTech, Healthcare and Longevity Matter Now

AI foundation models and generative systems are increasingly being applied to biology and medicine. AI is accelerating target discovery, molecule design, biomarker identification, clinical trial design, diagnostics, patient stratification and the operational management of care systems. At the same time, healthcare remains a highly regulated and high-stakes domain. This means that competitiveness depends not simply on AI talent or computing infrastructure, but on whether ecosystems can validate, govern and scale AI responsibly.

The report frames AI in BioTech, Healthcare and Longevity across three overlapping domains:

• AI in BioTech: drug discovery, genomics, molecule design, biomarker discovery and translational R&D.

• AI in Healthcare: diagnostics, clinical workflows, hospital systems, decision support, patient management and operational healthcare readiness.

• AI in Longevity: preventive healthcare, risk prediction, aging biomarkers, personalized prevention, healthspan optimization and the ability to scale AI-enabled longevity solutions through clinics, investment platforms and digital infrastructure.

  
  

The Index separates AI in BioTech, AI in Healthcare and AI in Longevity while assessing their cross-domain impact. This analytical distinction is especially important for Hong Kong, where the ecosystem’s value lies not merely in R&D density, but in the convergence of capital formation, healthcare quality, governance credibility and regional connectivity.

www.hk-ecosystem.tech

Speaker Perspectives from the Press Launch

The press launch made the report’s core thesis practical: AI competitiveness in life sciences is not a static ranking exercise. It is a question of whether countries and city hubs can build trusted, deployable systems. The committee discussion helped translate the report’s methodology into real policy, investment and ecosystem implications.

Dame Jenny Shipley

https://www.youtube.com/watch?v=_1GAyg_W4s8

Dame Jenny Shipley — Former Prime Minister of New Zealand; Member of the Global AI Competitiveness Index Committee.

• Emphasized the value of the Index for governments, health ministers and public servants deciding how to deploy AI across real systems.

• Positioned the report as practical deployment intelligence rather than a general “AI everything” narrative.

• Highlighted the importance of bringing together infrastructure, human capital, financial capital, policy alignment and applied implementation rather than relying on isolated pilots.

• Discussed China’s scale and data-linkage capacity in healthcare AI, while also describing Hong Kong as an important interface between China, the Greater Bay Area and global innovation networks.

  
  

Dr King Au

https://www.youtube.com/watch?v=VeLmq39cmlg

Dr King Au — Former Executive Director, Hong Kong Financial Services Development Council; Member of the Global AI Competitiveness Index Committee.

• Explained Hong Kong’s high city-hub ranking through its combination of financial-market depth, institutional credibility, healthcare capacity and Greater Bay Area connectivity.

• Described Hong Kong as a super-connector linking finance, data, talent, scale and innovation.

• Clarified that the Index’s longevity pillar is not a simple life-expectancy measure, but an assessment of AI-enabled longevity capacity, including biomarkers, risk prediction, clinical deployment and commercialization.

• Highlighted practical mechanisms including drug-approval pathways, public-hospital adoption, cross-boundary data flows, medical-data standards and public-private coordination.

Dmitry Kaminskiy

https://www.youtube.com/watch?v=p2hyZIXC7zI

Dmitry Kaminskiy — General Partner, Deep Knowledge Group; Member of the Global AI Competitiveness Index Committee.

• Framed AI in life sciences as a transition from experimentation to infrastructure.

• Emphasized that the strongest biomedical AI systems combine research, governance, clinical validation, capital formation and commercialization pathways.

• Discussed fast-rising hubs such as the UAE and Abu Dhabi, noting that younger, focused and well-resourced ecosystems can rise quickly when deployment, capital and regulation develop together.

• Positioned Hong Kong as a biomedical AI gateway because of its governance credibility, capital-market depth and regional leverage.

Prof. Dr. Patrick Glauner

https://www.youtube.com/watch?v=pMCJP1wmTTo

Prof. Dr. Patrick Glauner — Professor of Artificial Intelligence, Deggendorf Institute of Technology; Member of the Global AI Competitiveness Index Committee.

• Introduced the report’s central finding: biomedical AI leadership depends on deployment-readiness, not research scale alone.

• Summarized the country and city-hub rankings and the logic behind the Part 6 methodology.

• Emphasized governed data access, clinical validation, regulatory capacity and commercialization as decisive factors in life-science AI competitiveness.

• Moderated the launch discussion and helped frame the report as a benchmark of real-world biomedical AI capacity.

Hong Kong as a Biomedical AI Coordination and Scaling Hub

The same-day launch of AI in BioTech, Healthcare and Longevity in Hong Kong provides a jurisdiction-specific complement to the global Index. Where the Global AI Competitiveness Index supplies international comparison, the Hong Kong report examines local infrastructure, institutions, investors, corporate actors, clinical capacity and ecosystem logic in far greater detail.

The Hong Kong report maps and analyzes the city’s emerging AI-enabled life-sciences ecosystem across research, translational capacity, clinical data readiness, governance, regulatory pathways, capital formation, public programmes and ecosystem coordination.

The report’s core thesis is that Hong Kong’s life-science AI advantage is not built on scale alone. It is built on a distinctive combination of institutional credibility, capital markets and cross-border connectivity — especially its interface with the Greater Bay Area and mainland China.

“Hong Kong really is a super-connector, connecting not just financial markets, but data, talent, scale and innovation.”

— Dr King Au, Former Executive Director, Hong Kong Financial Services Development Council

The accompanying platform maps 330 organizations, including 145 companies, 90 investors, 40 hubs, 32 publicly traded corporations, 12 clinics, six universities and six conferences. The company landscape spans AI in biotech, AI in healthcare and AI in longevity, showing the breadth of actors involved in the city’s ecosystem.

Mapped database snapshot for AI in BioTech, Healthcare and Longevity in Hong Kong.

AI for BioTech, Healthcare and Longevity in Hong Kong

Coinciding with the latest edition of the Global AI Competitiveness Index, Deep Knowledge Group also launched AI in Biotech, Healthcare and Longevity in Hong Kong, a new industrial ecosystem platform and report released today by Deep Knowledge Group, represents the more thorough and strategically actionable mapping and analysis of the combined BioTech, Healthcare and Longevity Ecosystem produced to date, covering 330 key entities.

The project builds upon on the successful release of the AI in Hong Kong industrial ecosystem platform and report (with the Hong Kong AI Industry Association serving as a Supporting Partner) and the Q1 2026 release of AI for Finance in Hong Kong, both produced by Deep Knowledge Group (DKG) with the Hong Kong Financial Services Development Council (FSDC) as Formal Observer.

The relationship between the two releases mirrors the structure of earlier DKG launches in Hong Kong. The global Index provides a macro benchmark; the Hong Kong ecosystem platform shows the microstructure that helps explain the city’s position. This pattern began with the AI Industry Ecosystem in Hong Kong platform and continued with AI for Finance in Hong Kong. Part 6 extends the same model into life sciences: the global benchmark identifies leadership patterns, while the local report shows the institutions, projects and actors that underpin one leading hub.

John Lee’s AI Index Citation and Hong Kong’s Broader AI Narrative

https://www.youtube.com/watch?v=_CfN7URWSfU

Hong Kong Chief Executive John Lee cites the Global AI Competitiveness Index during the WIC Asia-Pacific Summit.

The launch also comes in the context of growing public recognition of Hong Kong’s AI competitiveness. In April 2026, Hong Kong Chief Executive John Lee cited the Global AI Competitiveness Index in his opening speech at the WIC Asia-Pacific Summit, referencing Hong Kong’s ranking in an earlier DKG benchmark. This public citation gave additional visibility to the Index within Hong Kong’s evolving AI narrative and reinforced the relevance of DKG’s city-level competitiveness analysis.

Lee’s speech emphasized Hong Kong’s common-law system, free flow of information and capital, the Greater Bay Area, the Hetao Shenzhen-Hong Kong Science and Technology Innovation Co-operation Zone, computing infrastructure, capital-market strengths and the city’s ambition to become an international innovation and technology centre. These themes align closely with the kinds of enabling conditions that the Index examines across its thematic editions.

Key Findings from the Hong Kong Report

• Hong Kong has a meaningful concentration of healthcare AI capability and enabling institutions, supporting practical adoption pathways.

• Capital-market infrastructure creates a strong scaling thesis for AI-native biotech and healthtech companies.

• The city’s Greater Bay Area interface allows Hong Kong to act as a coordination node, connecting international capital and governance credibility with regional scale advantages.

• The highest-return priorities include repeatable clinical AI validation pathways, stronger data interoperability, improved translational throughput, deeper growth-stage and strategic capital participation, and more visible coordination across the ecosystem.

The Strategic Meaning of Longevity AI

One of the most distinctive features of Part 6 is its inclusion of longevity and preventive-health innovation as a core pillar. Longevity AI is not treated as a wellness category or as a simple life-expectancy measure. It is evaluated through AI-enabled capability: aging biomarkers, risk prediction, clinical deployment, commercialization, capital formation and the infrastructure needed to scale preventive-health and longevity solutions.

For Hong Kong, this is especially relevant. The city already has exceptional population-health indicators and a strong healthcare system, but its next opportunity lies in converting longevity and preventive-health leadership into a more explicit AI-enabled ecosystem advantage.

Biomedical AI Requires Trust, Regulation and Capital Formation

The report also includes wider committee perspectives on the conditions that make biomedical AI different from general AI adoption. In life sciences, competitiveness depends on more than algorithms. It depends on clinical validation, governed data access, regulatory reliability, investment depth and commercialization pathways. Those are the variables that determine whether AI can move safely into real systems.

Strategic implications slide summarizing why biomedical AI competitiveness matters for policymakers, investors and ecosystem builders.

From Rankings to Actionable Intelligence

The launch of these two reports demonstrates the evolving role of the Global AI Competitiveness Index series. Rankings are important, but the deeper value lies in the analytical architecture behind them: identifying the conditions under which AI becomes practically deployable, commercially scalable and institutionally trusted.

• For policymakers, the Index shows where AI deployment capacity is actually forming.

• For investors, it identifies jurisdictions and hubs where scientific capability, data access, regulatory credibility and capital pathways converge.

• For healthcare and life-science leaders, it clarifies where AI is closest to clinical and translational implementation.

• For Hong Kong, it provides both validation and a strategic agenda: what is already strong, what remains underdeveloped, and where coordinated action can most efficiently strengthen competitiveness.

Conclusion: The Next Phase of AI Competitiveness

The launch of Global AI Competitiveness Index Part 6 and AI in BioTech, Healthcare and Longevity in Hong Kong marks an important continuation of DKG’s broader AI competitiveness programme.

The global report shows that the frontier of AI competition is moving into regulated, high-stakes and socially consequential sectors. The Hong Kong report shows how one leading city-level hub is positioning itself within that shift. Taken together, the releases reinforce a clear conclusion: the next phase of AI leadership will not be won by jurisdictions that merely produce models, publish papers or attract startups. It will be won by those that can govern, validate, fund and integrate AI into real biomedical systems.

“In biomedical AI, competitiveness is not measured by promising models alone. It depends on whether those models can be validated, integrated into clinical and translational workflows, and governed within trusted institutional systems.”

— Prof. Dr. Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology

For Hong Kong, the opportunity is to become one of Asia’s leading biomedical AI coordination and scaling hubs: a place where institutional credibility, capital markets, clinical systems and Greater Bay Area leverage converge. For the wider Index series, Part 6 shows that sector-specific AI competitiveness benchmarking is becoming increasingly important as AI diffuses into real industries and public systems.

Conclusion: From Sector-Specific AI Competitiveness to the Annual Global Benchmark

The launch of Global AI Competitiveness Index Part 6 marks the next stage in the AI Index Consortium’s broader benchmarking programme: moving from general AI capacity toward sector-specific intelligence on how AI is deployed in real economies, regulated systems and high-impact industries. Part 6 shows that biomedical AI leadership depends not only on models, research or capital, but on the ability to validate, govern, finance and integrate AI into practical healthcare, biotechnology and longevity systems.

The next planned edition of the Index will focus on AI for Social Good and Sustainability, extending the series into the public-benefit dimension of AI competitiveness: responsible deployment, public services, social infrastructure, green technology, climate resilience, institutional coordination and SDG-aligned outcomes.

By the end of 2026, the AI Index Consortium also plans to issue an annual aggregated edition of the Global AI Competitiveness Index, consolidating insights from the previous thematic editions into the series’ most comprehensive benchmark to date. This annual edition will provide a broader synthesis of national and city-level AI competitiveness across enterprise activity, research innovation, human capital, governance, finance, life sciences and social-good deployment.

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