Digitalization and sustainability are popular words within modern disciplines as practitioners each look toward the future of their respective fields. Specifically for the African continent, which is making great strides in developmental targets, those two terms are central to core aspects of policy initiatives that may foster cooperation across its varied lands and nations. One of the underlying challenges that confront Africa is a lack of strong regional integration across socioeconomic and political programs; there is value in African regions having more regional connectedness. We assess the rate of regional integration and development in Africa and discuss how to alleviate development crises that could be accelerated by deploying a sustainable cybersecurity strategy, which increasingly includes the bioeconomy and its components. This can be done through the application of Fourth Industrial Revolution (4IR) technologies such as Artificial Intelligence (AI) and modern biotechnology. This work suggests that political and socio-economic activities associated with regional integration must be seen as an all-encompassing task that transcends beyond national boundaries towards a cyber biodefense fortification and increases in 4IR technological integration. This has the aim of thereby encompassing efforts to persuade leaders to fast-track policies that seek to promote geospatial cyberinfrastructure, integrative cybersecurity considerations, cross-border digitalization programs, and increased need for cybersecurity research and education, with mindfulness towards education and further integration of mindful automation. In conclusion, a model of integrative security is proposed for Africa.

A rapid and accurate diagnosis of medical conditions like cardiomegaly and pleural effusion is of the utmost importance to reduce mortality and medical costs. Artificial Intelligence has shown promise in diagnosing medical conditions. With this study, we seek to evaluate how well Artificial Intelligence (AI) systems, developed my minoHealth AI Labs, will perform at diagnosing cardiomegaly and pleural effusion, using chest x-rays from Ghana, Vietnam and the USA, and how well AI systems will perform when compared with radiologists working in Ghana. The evaluation dataset used in this study contained 100 images randomly selected from three datasets. The Deep Learning models were further tested on a larger Ghanaian dataset containing five hundred and sixty one (561) samples. Two AI systems were then evaluated on the evaluation dataset, whilst we also gave the same chest x-ray images within the evaluation dataset to 4 radiologists, with 5–20 years experience, to diagnose independently. For cardiomegaly, minoHealth.ai systems scored Area under the Receiver operating characteristic Curve (AUC-ROC) of 0.9 and 0.97 while the AUC-ROC of individual radiologists ranged from 0.77 to 0.87. For pleural effusion, the minoHealth.ai systems scored 0.97 and 0.91 whereas individual radiologists scored between 0.75 and 0.86. On both conditions, the best performing AI model outperforms the best performing radiologist by about 10%. We also evaluate the specificity, sensitivity, negative predictive value (NPV), and positive predictive value (PPV) between the minoHealth.ai systems and radiologists.