International journal of MCH and AIDS
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Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. ⋯ Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research.
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The population with emerging diseases such as COVID-19, which is used to calculate the basic reproduction number of epidemic outbreak (R0 ) cannot be simply observed. In this article, we have proposed a method for estimating the hidden population of people with COVID-19 disease. ⋯ The provision of medical equipment (e.g., masks, alcohol, ventilators, medication, etc.), the reopening of schools and universities, the start of tourism and public gatherings, the provision of medical staff and preventive planning depend on the number of patients with the disease. Therefore, it is very important to estimate the number of patients.
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As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. ⋯ Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US.
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Dramatic increases in opioid and drug overdose mortality have occurred in the United States (US) over the past two decades. To address this national public health crisis and identify gaps in the literature, we analyzed recent empirical trends in US drug overdose mortality by key social determinants and conducted a selective review of the recent literature on the magnitude of the opioid crisis facing different racial/ethnic, socioeconomic, and rural-urban segments of the US population. ⋯ Our analysis and review indicate substantial disparities in drug overdoses and related mortality, pain management, and treatment outcomes according to social determinants. Increases in drug overdoses and resultant mortality are not only unique to the US, but have also been observed in other industrialized countries. Healthcare systems, community leaders, and policymakers addressing the opioid epidemic should focus on upstream structural factors including education, economic opportunity, social cohesion, racial/ethnic disadvantage, geographic isolation, and life satisfaction.