Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.
Artificial intelligence (AI) can improve various aspects of healthcare. It can help reduce annual expenditure, allow early detection of diseases, provide round-the-clock monitoring for chronic disorders, and help limit the exposure of healthcare professionals in contagious environments. The use of AI in healthcare systems in Africa, in particular, can eliminate inefficiencies such as misdiagnosis, shortage in healthcare workers, and wait and recovery time. However, it is important to safeguard against issues such as privacy breaches, or lack of personalised care and accessibility. The central tenet for an AI framework must be ethics. This brief discusses the benefits and challenges of introducing AI in Africa's healthcare sector and suggests how policymakers can strike a balance between allowing innovation and protecting data. This paper is for ORF's Centre for New Economic Diplomacy (CNED).