Cholera continues to pose a significant public health challenge in Nigeria, driven by poor sanitation, inadequate water quality, and climatic factors that create favorable conditions for outbreaks. Since the first epidemic in 1972, Nigeria has experienced recurrent outbreaks, with the most severe in 1991, resulting in over 7,000 deaths. Current surveillance systems and diagnostic methods are limited by infrastructural gaps, insufficient skilled personnel, and inadequate reporting, leading to delays in outbreak detection and response. These limitations exacerbate the public health burden, increasing mortality and the economic impact of cholera epidemics. This paper explores the potential of artificial intelligence (AI) and machine learning (ML) to address these challenges. AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. Case studies from other cholera-endemic regions, where AI achieved high predictive accuracy, demonstrate its transformative potential. By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. However, challenges such as data quality, standardization, and infrastructural deficits must be addressed. Multi-sectoral collaboration involving public health authorities, AI specialists, and policymakers is essential for the successful deployment of these technologies. This article concludes that AI-powered cholera surveillance systems have the potential to revolutionize public health outcomes, reducing cholera-related morbidity and mortality in resource-limited settings like Nigeria.

This study aims to assess a new mobile application (app)’s efficacy in raising oral cancer awareness among older adults through educational videos and serious games. The app, named TEGO^® (Tele-platform of Geriatric and Dental Specialties), with a video about oral cancer prevention, oral-self-examination, and serious gaming elements, like trivia and word search puzzles to reinforce the acquired knowledge was developed. Fifty-six patients, aged 60 to 80 years, were randomly selected from the Dental Clinic of the University of Chile and invited to use the app on their personal smartphones. Knowledge and attitudes were evaluated before two and four weeks after use. Oral self-examination practices were measured with a checkup guideline. The participation rate was 41.1%, mostly male (52.2%). Before using the app, 30.4% of the participants reported awareness of oral cancer, and none had performed oral self-examinations. Following two weeks after use, there was notable engagement, with 100% of participants utilizing it and responding that they had heard about oral cancer, and 56.5% having practiced an oral self-examination. This last outcome increased to 82.6% in the fourth week. The use of mHealth technologies has the potential as an effective educational tool for disseminating knowledge about oral cancer among older adults.

Confocal laser endomicroscopy (CLE) enables real-time diagnosis of oral cancer and potentially malignant disorders by in vivo microscopic tissue examination. One impediment to the widespread clinical adoption of this technology is the need for operator expertise in image interpretation. Here we review the application of AI to automatic tissue classification of CLE images and discuss the opportunities for integrating this technology to advance the adoption of real-time digital pathology thus improving speed, precision and reproducibility.