Globalizing the AI Revolution in Health Care
Machine learning and big data promise to make the process of discovering and applying new cancer treatments faster and more effective than ever. But to realize these technologies' potential, we will need pragmatic, globally standardized policies governing the collection and use of medical data.
MUNICH – We are entering a transformational period in medical science, as traditional research techniques combine with massive computing power and a wealth of new data. Just recently, Google announced that it has developed an artificial intelligence (AI) system capable of outperforming human radiologists in detecting breast cancer. And that is merely the latest example of how machine learning and big data are leading to new medical diagnostics, treatments, and discoveries. To realize AI’s enormous potential, however, we must develop a pragmatic and globally agreed approach to governing the collection and use of “real-world data.”
Real-world data includes any information that can help to guide new medical research. Some of it has been around for quite a while. For example, cancer researchers have long used anonymized health records to select patient candidates who are most likely to respond well to novel and experimental treatments. But other kinds of data have become available only recently, along with the technology for analyzing them at scale.
The new capabilities offered by AI and related technologies raise complicated, sometimes controversial questions about privacy and data ownership. But we can meet these challenges by establishing comprehensive rules to safeguard personal information. Policymakers around the world and within global-governance institutions must not delay. Medical science powerhouses are already forging ahead with real-world data initiatives in the United States, where the wide availability of anonymized patient data is fueling a new wave of innovation.
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