Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases

The report argues that while AI has transformative potential for global health, its impact is hindered by a focus on small-scale experimentation rather than scalable implementation. It prioritizes three key areas for immediate public-private acceleration: AI-driven diagnosis and risk stratification, infectious disease intelligence, and clinical trial optimization. The document identifies four primary barriers to value realization: poor data foundations, lack of scalability, inadequate technological infrastructure, and low trust. To address these, it advocates for the use of representative data, low-friction adoptability, and cross-border transferability, calling for coordinated action from governments, providers, payers, and technology firms.

• Healthcare is currently characterized by an excess of small-scale pilots that fail to scale, leading to a situation where many AI initiatives do not achieve their desired value despite being viewed as important by leaders.
• Three systemic trends are accelerating AI adoption: the exponential growth of medical data (predicted to exceed 10 zettabytes by 2025), a global shortfall of 10 million healthcare workers by 2030, and advances in deep learning and generative AI.
• The report prioritizes three specific AI use cases for immediate public-private acceleration due to their high potential for global health impact and need for multi-sectoral collaboration: AI-driven diagnosis and risk stratification, clinical trial optimization, and outbreak intelligence and prediction.
• AI-driven diagnosis and risk stratification can significantly improve outcomes; for example, AI tools can detect early signs of pancreatic cancer with 86% accuracy up to three years earlier than doctors.
• Infectious disease intelligence uses AI as an early warning system by monitoring non-traditional data sources, such as local news and mobility data, to predict pathogen spread and climate-related health risks.
• AI can optimize clinical trials by improving the recruitment of diverse participants and predicting dropout risks, addressing the fact that approximately 80% of clinical trials fail to meet recruitment targets.
• Four primary barriers prevent AI from realizing its full value in healthcare: poor data foundations (cited by 75% of experts), lack of scalability and cooperation, inadequate technological infrastructure, and low trust (cited by 80% of experts).
• To accelerate AI adoption, the report proposes three foundational principles: ensuring data is usable and representative, designing for low-friction adoptability into existing workflows, and ensuring easy scalability across borders, particularly to low- and middle-income countries.
• Administrative AI has a strong value proposition in high-income regions like the US and EU, where administrative tasks can account for roughly a third of healthcare costs and up to half of a provider's time.

APA

World Economic Forum (2023). Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases. https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf

Chicago

World Economic Forum. Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases. 2023. https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases |date=June 2023 |url=https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2023scaling, author = {{World Economic Forum}}, title = {{Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases}}, institution = {World Economic Forum}, year = {2023}, month = jun, url = {https://www3.weforum.org/docs/WEF_Scaling_Smart_Solutions_with_AI_in_Health_Unlocking_Impact_on_High_Potential_Use_Cases.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }