The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings.

Despite repeated efforts by the Nigerian government and the international community, under-5 child mortality remains alarmingly high in Nigeria. This narrative review aims to reassess the key factors contributing to this persistent public health challenge. A comprehensive search of peer-reviewed articles and reports published in English was conducted to identify and synthesize data on the factors predisposing Nigerian children under 5 to mortality. The review identifies multiple interrelated contributors, including socioeconomic, sociocultural, and demographic factors, inadequate access to healthcare services, an under-resourced healthcare system, and a shortage of qualified healthcare professionals. The high burden of communicable and preventable diseases also plays a significant role in under-5 mortality. To address these issues, targeted interventions such as improving healthcare access, strengthening the health system, and reducing poverty are essential. The findings underscore the urgent need for a coordinated, multi-sectoral approach to effectively reduce under-5 mortality in Nigeria and improve the health outcomes of vulnerable children. Government, healthcare providers, and communities must work together to address these concerns so that all children can access the care they need to live and flourish.

Maternal mortality remains one of Nigeria's most significant public health challenges. In order to address this issue sustainably, it is necessary to consider the perceptions of all stakeholders involved, including midwives. To examine the determinants of maternal mortality in south-western Nigeria from the midwife's perspective. A cross-sectional study was employed using mixed methods with a semi-structured questionnaire and an in-depth interview guide. Quantitative data were obtained from 215 midwives using a convenience sampling technique. Qualitative data were obtained from 25 midwives from five government health centres, selected using a stratified sampling technique. Quantitative data were analysed using SPSS Version 20 using descriptive and inferential statistics with 95 % confidence intervals, while qualitative data were analysed using thematic analyses. The mean age and work experience of the participants were 35.2 ± 9.3 years and 8.4 ± 7.0 years, respectively. The midwives perceived that the main determinants of maternal mortality were postpartum haemorrhage (86.5 %), hypertensive disorder in pregnancy (80.9 %), mismanagement at mission homes/traditional birth attendant centres (MH/TBAs) (79.1 %) and sepsis (70.1 %). Some of the priority target areas to improve the well-being of pregnant women as identified by the midwives, were increased awareness of pregnancy danger signs (97 %), destigmatising caesarean section (CS) (96 %), regulation of MH/TBAs (92 %), and increased accessibility of hospitals (84 %). Findings from the qualitative data also affirmed that regulating MH/TBAs, destigmatising CS and subsidising healthcare expenses were prerequisites to curbing maternal mortality. Inferential analysis revealed that determinants such as unsafe abortion (p < 0.001), ectopic pregnancy (p = 0.001), domestic violence (p = 0.023), malaria (p = 0.029), short interbirth interval (p = 0.03), and patients' negative perceptions of CS delivery (p = 0.036) were more commonly perceived to be associated with maternal mortality by younger midwives (age 17-34 years) compared with older midwives. The results indicate that resolving the maternal mortality crisis sustainably in Nigeria will require increased accessibility to basic health care and health promotion campaigns to counteract unhelpful sociocultural norms. Future interventions must be tailored to address both traditional and emerging causes of maternal mortality in southwestern Nigeria.

An N95 respirator is designed to fit tightly and filter airborne particles. Healthcare workers (HCWs), especially those handling aerosols, must complete fit testing to choose the right mask and receive proper training. Traditionally, Occupational Safety and Health Administration (OSHA)'s eight-step protocol was used for N95 fit testing, but in 2019, a shorter four-step version was introduced, cutting testing time. Although the OSHA claims both protocols are equivalent, there is limited empirical support and no research on the reproducibility of the conventional method.The proposed cross-sectional observational study will evaluate the equivalence (i.e. agreement) and reproducibility of the fit-testing results of three different designed respirators tested among Chinese HCWs using both new and conventional fit-testing protocols.The kappa agreement between the new and conventional protocols can serve as an important point of reference for fit-testing policies. If there is a high level of equivalence, both fit-testing protocols could be simultaneously implemented as they would be expected to produce similar results. In contrast, if the conventional protocol is more stringent than the new protocol, conservatively, it would be necessary to exercise caution and suspend implementation of the new protocol. Additionally, the reproducibility of respirator fit testing must be determined. Examining the reproducibility could eliminate uncertainty regarding the ability of a respirator to maintain a proper fit in a real clinical setting. This study was registered to ISRCTN registry: ISRCTN15612510. The study was retrospectively registered on 28^th May 2025; corresponding link: https://doi.org/10.1186/ISRCTN15612510.