Ministers worldwide are facing a dual crisis of time and funding in their health sectors. An insider perspective reveals a common theme: overflowing inboxes filled with complaints, warnings about budget shortfalls from finance ministries, and urgent requests from prime ministers for “quick wins.” The reality, however, is that swift solutions in healthcare are scarce. Global health systems are grappling with staffing shortages, rising demand, and budgets that are failing to meet these challenges. With over 4 billion people lacking access to essential health services, the pressure is mounting for systems to achieve more with less.
In this context, artificial intelligence (AI) is being touted as a potential remedy, but many leaders are unsure of where to begin. The challenge lies not only in identifying promising AI applications but also in determining how to transform these possibilities into tangible results. To assist governments, a practical framework has been introduced, offering a structured way to assess the most useful applications of AI in healthcare.
Governments are already utilizing AI for various purposes, including disease surveillance and determining patient treatment needs. Those who adopt AI strategically can bypass outdated infrastructures, advancing toward more integrated and responsive healthcare systems. However, many transformative applications—such as decision support for clinicians and population health analytics—demand significant governmental investment and oversight to maximize their public value. Nonetheless, leaders often struggle to prioritize effectively, with some focusing narrowly on high-profile use cases like medical imaging, while others are overwhelmed by a surge of competing AI products that complicate decision-making.
The Tony Blair Institute for Global Change has engaged with governments across 26 countries to explore effective applications of AI in healthcare. Frequently, leaders express a desire to harness AI tailored to their specific needs rather than conform to vendor-driven priorities. To navigate this landscape, a clear, practical approach is necessary, classifying AI applications in a way that aligns with political realities and supports effective decision-making.
Framework for AI in Healthcare
The AI in Health Framework developed by the Institute aims to fill this gap, providing a comprehensive classification of AI applications relevant to healthcare delivery. The framework offers a structured taxonomy organized into six overarching domains: Diagnostics, Clinical Care, Patient Self-Care, Public Health, Resource Management, and Supporting Systems. Each domain encompasses sub-categories that account for a total of 23 distinct applications. This framework is expected to evolve alongside advancements in AI technology.
In the Diagnostics domain, AI supports health professionals through imaging and lab tests. For instance, AI-driven tools can analyze X-ray images to detect conditions such as tuberculosis, while automated microscopes identify malaria parasites in lab samples. In Clinical Care, AI assists clinicians by providing decision support and monitoring patients’ health. Examples include AI systems that predict patient readiness for discharge or provide medication-dosing recommendations.
AI also empowers patients through self-care tools like wearables and mobile applications, assisting with chronic illness management and proactive health monitoring. In Public Health, AI enhances disease surveillance and facilitates outbreak response efforts. Resource Management employs AI to optimize healthcare facilities’ capacities and manage supply chains effectively. Finally, Supporting Systems streamline administrative tasks, such as billing and patient scheduling, improving overall healthcare efficiency.
Rwanda serves as a case study in leveraging AI for health improvements. The Rwandan government, prioritizing health within its national development agenda, has recognized AI as a crucial tool for addressing workforce shortages and enhancing health information systems. Early initiatives include the deployment of drones for blood delivery and the establishment of a national electronic medical records system. Despite these advances, challenges such as data readiness, capacity constraints, and the need for alignment with existing systems remain. The AI in Health Framework has been instrumental in helping Rwanda classify and prioritize AI applications based on its specific healthcare challenges.
As governments worldwide grapple with the complexities of implementing AI in their health systems, the emphasis is not on whether to use AI, but rather on how to do so effectively. The AI in Health Framework provides actionable insights, allowing leaders to prioritize healthcare challenges and select AI solutions that yield real benefits. With the right foundations in place, AI can transform healthcare delivery, catalyzing significant improvements in service accessibility and quality for populations in need.
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