Artificial Intelligence (AI) is generating a lot of buzz across all sectors, and global health is no exception. Many see AI as a promising tool that could transform health systems, making them more effective and accessible. Like any breakthrough technology, however, AI comes with its own set of implementation challenges and unknowns. The good news? While we’re still in the early stages of understanding how to harness its potential, the global health field is not starting from scratch. We can learn from past innovations. By applying lessons from those efforts – what’s worked and what hasn’t – we can shape better, more practical strategies to ensure AI supports improved health and wellbeing worldwide.

Here are five lessons from other innovations that can guide the way we apply AI in global health:

  1. Be wary of the parallel system

    Wherever possible, innovations should strengthen existing systems rather than bypass them. New tools that rely on entirely separate delivery systems may show impressive results in controlled pilots but prove nearly impossible to scale (another tip for another time—be wary of pilot-itis!). For HIV care, integrating services into primary healthcare in countries across Africa has been shown to improve patient outcomes, increase retention, and strengthen broader health systems.[i] When working on AI-driven innovations, ask yourself how you can build on existing foundations, improving what’s already in place to ensure lasting impact.

  2. Measure what matters

    When working with clients on advocacy strategies, the first (or rather, the first four or five) conversations I have with them are focused on defining their audience—who they are, what they care about, and what will move them. In previous work on new vaccine introductions in Africa and Asia, I witnessed too many people pitch the wrong message to the wrong person—like making a case for long-term health benefits to a Minister of Finance focused on next year’s budget gap.

Read the full article at the Global Washington blog.