Recognising the huge potential impact of AI in global health, The Rockefeller Foundation and United States Agency for International Development's (USAID) Center for Innovation and Impact (CII) partnered, in close coordination with the Bill and Melinda Gates Foundation, developed and launch today a groundbreaking new report: AI in Global Health: Defining a Collective Path Forward. In the past decade emerging technologies have been flooring the gas petal driving exponential innovation in almost all fields and industries. From blockchain and cryptocurrencies, to getting your groceries delivered by drone (UAVs) on your front lawn, to using artificial intelligence (AI) as a Swiss Army knife to'fix' everything, new technologies present significant potential to bring real and meaningful change, especially in healthcare. Technologies like AI are rapidly evolving, yet are still at an early-stage, which provides the development ecosystem the unique opportunity to take an active part in shaping the market to make sure new technologies are introduced and scaled effectively and appropriately. The report states it's important to acknowledge that AI is a means to an end for global health impact and not always the right or best solution for every health challenge.

Prior work poses a diversity paradox for science. Diversity breeds scientific innovation, and yet, diverse individuals have less successful scientific careers. But if diversity is good for innovation, why is science not rewarding diversity? We answer this question by utilizing a near-population of ~1.03 million US doctoral recipients from 1980-2015 and their careers into publishing and faculty roles. The article uses text analysis and machine learning techniques to answer a series of questions: How can we detect scientific innovation? Does diversity breed innovation? And are the innovations of diverse individuals adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are discounted: e.g., innovations by gender minorities are taken up by other scholars at lower rates than innovations by gender majorities, and innovations by gender and racial minorities result in fewer academic positions. This suggests an unfair system in which diverse individuals innovate, but their innovations are disproportionately ignored and fail to convert into career success at the same rate as majority groups. In sum, there may be an unwarranted reproduction of stratification in academic careers that discounts diversity's role in innovation and partly explains the underrepresentation of some groups in academia.