Artificial intelligence (AI) is the simulation of the human mind in computer systems programmed to mimic the thinking of human minds and actions hence learn and solve problems. AI based computational pathology is an emerging discipline showing great promise in both accuracy and quality diagnosis of cellular based ailments for effective patient care.

Cervical Cancer is preventable, common yet highly fatal disease. Treatment of this disease relies heavily on early stage diagnosis. Unfortunately, this malady remains relatively asymptomatic until the later highly mortal stages. Screening has been typically accomplished by microscopic analysis of cytological samples (PAP smears) to detect pre-cancerous cellular lesions. The process requires laboratory infrastructure and scarce trained experts for sample analysis. Extensive screening programs are thus not available or even viable in resource poor countries. A low-cost point-of-care scanning microscope has been developed to capture digital images of microscopy samples and relay the images wirelessly over the internet for remote analysis. This is done by experts or image analysis supported AI. The digital data can be stored in a central cloud-based space allowing for remote access and manual review by pathologists or automated review by a data algorithm. This is a breakthrough for the application of data learning systems (DLS), and computational science into the world of pathology. The possibilities are indeed diverse and encompass classification of different cell atypia. In a recent study, the DLS achieved high sensitivity for general atypia (100%; 95% CI, 82.4%-100%) and for high-grade atypia (100%; 95% CI, 47.8%-100%), with corresponding specificities of 78.4% (95% CI, 73.6%-82.4%) and 93.3% (95% CI, 90.1%-95.6%), respectively.

Momic and AI in cytology is a reliable tool that will make cervical cancer screening accessible and potentially improve the sensitivity and accuracy of diagnoses and turn-around-times. Its application is exciting, changing and improving the current health care landscape.

I'll intend to provide an overview of
innovations in digital science technologies and novel
analytical tools, such as artificial intelligence and
machine learning, which will transform the availability
of and access to imaging diagnostics and aid decision
making.
Progress in technology, Augmented/Artificial intelligence (AI) and available data are creating unprecedented opportunities for prediction models to inform, personalise, and improve care This is already impacting practice in DI For example, AI can provide workflow enhancements that integrate with an electronic medical record to identify high risk or critical patients and prioritise them for care.

The paucity of the oncology workforce in sub-Saharan Africa makes it a herculean task to effectively address the growing burden of cancer morbidity and mortality on the continent. There have been concerted efforts over the years by governments, professional organizations, and academic institutions to train more experts in the field. Unfortunately, while some progress has been made in these efforts, it still falls short of addressing the magnitude of the crises. A recent study done by Vanderpuye et al, shows that there are only about 1 oncologist to 2,700 cancer patients in sub-Saharan Africa compared to 1 to 187 in the United States. There is an urgent need to leapfrog these gaps with innovative technologies that are centered on artificial intelligence (AI). The development and use of AI-powered technologies in high income countries to address the cancer burden is fast growing, and could be a useful tool to augment efforts by the oncology workforce in Africa. Research funding in AI in cancer in sub-Saharan Africa would not only help to mitigate these gaps, it would also provide valuable data that could potentially solve the cancer data gap in Africa and address data bias in machine learning models for cancer applications in high income countries. In this presentation ,we identify the growing opportunities for funding and research in this relatively novel landscape.