3 research outputs found
What We Know So Far: Artificial Intelligence in African Healthcare
Healthcare in Africa is a complex issue influenced by many factors including
poverty, lack of infrastructure, and inadequate funding. However, Artificial
intelligence (AI) applied to healthcare, has the potential to transform
healthcare in Africa by improving the accuracy and efficiency of diagnosis,
enabling earlier detection of diseases, and supporting the delivery of
personalized medicine. This paper reviews the current state of how AI
Algorithms can be used to improve diagnostics, treatment, and disease
monitoring, as well as how AI can be used to improve access to healthcare in
Africa as a low-resource setting and discusses some of the critical challenges
and opportunities for its adoption. As such, there is a need for a
well-coordinated effort by the governments, private sector, healthcare
providers, and international organizations to create sustainable AI solutions
that meet the unique needs of the African healthcare system.Comment: 8 pages, 1 figure, AAAI-23 conference in Washington, DC,
International Workshop on the Social Impact of AI for Africa(SIAIA
Computational methods applied to syphilis: where are we, and where are we going?
Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions