Executive summary Over the past decade, healthcare and technology industry leaders, public officials and researchers have placed high hopes on harnessing advances in artificial intelligence (AI) and machine learning (ML) to transform healthcare. What’s been lacking, however, is a way for multilateral stakeholder coalitions to unite around a common vision for the most attainable solutions that can build trust and confidence in providers, patients, policy-makers and business leaders. Why AI depends on multilateral cooperation Today’s global health and healthcare stakeholders face a perfect storm of systemic challenges. Healthcare consumers live in an era of worsening well-being and physical health due to the increasing burden of mental health conditions and chronic illnesses, putting serious strain on systems due to greater demand. At the same time, healthcare inefficiency, worker shortages and physician burnout are placing pressure on the supply of care, creating a vicious cycle, pushing costs to new heights and skyrocketing global healthcare expenditures estimated to have totalled $12 trillion in 2022. AI, defined by the World Economic Forum as “systems that act by sensing, interpreting data, learning, reasoning and recommending the best course of action”, represents an intelligent, scalable system to support healthcare leaders, decision-makers and practitioners in their quest to solve these challenges – if used safely and ethically. Yet, healthcare has been relatively slow to adopt AI-driven tools and solutions due to a growing tension between the incredible things AI makes possible and the human trust needed to put them to use. As such, the Forum has begun engaging stakeholders from across the Centre for Health and Healthcare to explore the transformative power of AI and ML against the backdrop of the digital transformation of health and healthcare, including by cultivating public-private collaboration to accelerate the responsible application of AI. This report aims to: – Create a shared taxonomy to express the breadth of healthcare applications for which AI is being used. – Identify use cases that are attainable now and have the greatest potential to improve global health outcomes through sustained publicprivate investment – Define the enablers most critical to AI’s responsible widespread adoption and scale-up in healthcare. Key takeaways – Three factors are driving the adoption of AI in healthcare: the exponential growth of medical data, a healthcare provider shortage (exacerbated, but not caused, by the COVID-19 pandemic) and advances in what AI technology is capable of. – Top use cases for sustained multilateral cooperation are in the areas of AI-driven diagnosis and risk stratification, clinical trial optimization, and outbreak intelligence and prediction. – Several additional areas, including administrative, workflow and training solutions; automated triage processes; supply chain and manufacturing; and drug discovery also deserve greater exploration and may hold equal promise. – To maximize the impact of AI in healthcare, data must be plentiful, useable and representative (to minimize bias); design must aid adoption by being transparent and inclusive, and applications must be seamless and scalable. – Even with sustained public-private investment, strong data foundations and thoughtful, ethical AI policies must be implemented to build trust and accelerate adoption in an appropriate way. Acting on this report’s recommendations will require coordinated effort between public- and private-sector leaders. Creating change in health and healthcare requires multilateral partnerships across health systems, consumers, governments and civil society. These technologies will transform how care is defined and delivered – but only if stakeholders can solve underlying issues with data foundations, refocus efforts towards scaling rather than experimentation and give providers, patients, policy-makers and business leaders the confidence to use them. AI offers hope for early disease detection, combating outbreaks and achieving breakthroughs in medicines. Collaboration is crucial to achieving this. Scaling Smart Solutions with AI in Health: Unlocking Impact on High-Potential Use Cases 4
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