Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled before-after, interrupted time series, comparative cohorts) evaluating CDSS in World Bank-defined LMICs. Standalone qualitative studies are excluded; mixed-methods studies are eligible only if they report comparative quantitative outcomes, for which we will extract the quantitative component. Searches (from inception to 30 September 2024) will cover MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, Global Health, Scopus, IEEE Xplore, LILACS, African Index Medicus, and IndMED, plus grey sources. Screening and extraction will be performed in duplicate. Risk of bias will be assessed with RoB 2 (randomized trials) and ROBINS-I (non-randomized). Random-effects meta-analysis will be performed where outcomes are conceptually or statistically comparable; otherwise, a structured narrative synthesis will be presented. Heterogeneity will be explored using relative and absolute metrics and a priori subgroups or meta-regression (condition area, care level, CDSS type, readiness proxies, study design).

Structural transformation, the shift from agrarian economies to more diversified industrial and service-based systems, is a key driver of economic development. However, in low- and middle-income countries (LMICs), data scarcity and unreliability hinder accurate assessments of this process. This paper presents a novel statistical framework designed to address these challenges by integrating Bayesian hierarchical modeling, machine learning-based data imputation, and factor analysis. The framework is specifically tailored for conditions of data sparsity and is capable of providing robust insights into sectoral productivity and employment shifts across diverse economies. By utilizing Bayesian models, uncertainties in data are effectively managed, while machine learning techniques impute missing data points, ensuring the integrity of the analysis. Factor analysis reduces the dimensionality of complex datasets, distilling them into core economic structures. The proposed framework has been validated through extensive simulations, demonstrating its ability to predict structural changes even when up to 60\% of data is missing. This approach offers policymakers and researchers a valuable tool for making informed decisions in environments where data quality is limited, contributing to the broader understanding of economic development in LMICs.

Group of Seven (G7) finance leaders have pledged to take action to maintain the stability of the global financial system after recent banking turmoil and to give low- and middle-income countries a bigger role in diversifying supply chains to make them more resilient. Their communique did not mention China by name but the supply-chain language fits in with "friend-shoring" efforts by industrial democracies to work with each other to become less reliant on the Asian manufacturing powerhouse for battery minerals, semiconductors and other strategic goods. "We commit to jointly empowering low- and middle-income countries to play bigger roles in supply chains through mutually beneficial cooperation by combining finance, knowledge, and partnership, which will help contribute to sustainable development and enhance supply chain resilience globally," the G7 finance ministers and central bank governors said in the statement on Wednesday. The finance chiefs of G7 nations – Canada, France, Germany, Italy, Japan, the United Kingdom and the United States – met on the sidelines of International Monetary Fund (IMF) and World Bank meetings in Washington, DC. They said they discussed recent financial sector developments after the failure of two United States banks and the forced sale of troubled global lender Credit Suisse. "We will continue to closely monitor financial sector developments and stand ready to take appropriate actions to maintain the stability and resilience of the global financial system," the G7 finance leaders said.

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.

Research into drugs to treat mosquito-borne flaviviruses such as Zika and dengue as well as COVID-19will benefit from a major funding boost, says a group of international scientists using artificial intelligence to discover new oral antivirals. A research consortium led by the non-profit COVID Moonshot has been awarded more than US$68 million from the US National Institutes of Health (NIH) to discover and develop globally accessible and affordable novel oral antivirals to combat COVID-19 and future pandemics. The development comes as monkeypox outbreaks have been declared around the world, raising concerns about the rapid spread of such viruses. Monkeypox is a viral disease that the World Health Organization says has emerged in at least 23 countries where the disease is not regularly found since 13 May. The open-science COVID Moonshot was established in 2020 with the objective of developing a safe, globally accessible and affordable antiviral pill for COVID-19.

Digital health products played a prominent role in addressing the COVID-19 pandemic and in helping caregivers and patients navigate their care in the past year. Going into 2022, remote monitoring, wearables, sensors, and other mobile health (mHealth) products are taking center stage in defining the future of medicine. "One of the clearest areas of excitement now and into the future is the sector of healthcare products referred to as wearables. These are devices like fitness trackers, heart monitors, and other devices that record in real time and communicate biometric data either directly to the user or to a connected platform for a variety of purposes, including coaching, intervention, analysis and even within clinical trials administration," notes a recent report from contract manufacturer Jabil, St. Petersburg, FL. The report, "Digital Health Technology Trends," finds that "the top three solution categories providers are developing or plan to develop are in patient monitoring, diagnostic equipment, and on-body or wearable devices (see Figure 1). As digital and mHealth capabilities have become an integral part of many medical devices and diagnostics, they have enabled a more agile and flexible healthcare system to emerge in the face of COVID-19. These products will continue to improve access to patient care. Digital transformation of healthcare is not just about adopting new digital technology, notes a recent position paper from medtech giant Philips. It's about reimagining healthcare for the digital age -- using the power of data, artificial intelligence (AI), cloud-based platforms, and new business models to improve health outcomes, lower the cost of care, and improve the human care experience for patients and staff alike."

According to the newly launched initiative, faster progress on AI is vital to make this happen, especially in low and middle-income countries, where the most lives are lost on the roads each year. AI can help in different ways, including better collection and analysis of crash data, enhancing road infrastructure, increasing the efficiency of post-crash response, and inspiring innovation in the regulatory frameworks. This approach requires equitable access to data and the ethical use of algorithms, which many countries currently lack, leaving them unable to identify road safety solutions. Announcing the initiative, the ITU Secretary-General, Houlin Zhao, said the disproportionate number of road deaths in developing countries "is yet another example of why the benefits of new technologies must reach everyone, everywhere". The new initiative aims to strengthen global AI efforts across the public and private sectors to improve safety for all road users -- whether traveling by automobile, motorcycle, bicycle, foot or other transportation modes.

For the first time, researchers from the London School of Hygiene & Tropical Medicine, Mercator Research Institute on Global Commons and Climate Change and the University of Leeds deploy machine learning algorithms to scan evidence on climate change and health across the world. Funded by the Foreign, Commonwealth and Development Office, they used machine learning to map the global published evidence on climate change, weather and health from 2013 to 2020 and produce an online interactive results platform. The approach identified the effects on health of air quality and heat to be the most frequently studied in an evidence base dominated by studies from high-income countries and China. There is currently very limited evidence from low- and middle-income countries that suffer most from the health consequences of climate change. Evidence on the impact of climate change on mental health and on maternal and child health is extremely limited.

Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in low- and middle-income countries. Diabetic retinopathy (DR) affects 1 in 3 people with diabetes and remains the leading cause of blindness in working-aged adults. There are 3 broad strategic imperatives to prevent blindness caused by DR. Primary prevention requires preventing or delaying the onset of DR in those with diabetes by systems-level lifestyle modifications such as increasing physical activity or dietary modifications, pharmacological interventions for glycaemic and blood pressure control, and systematic screening for the onset of DR. Secondary prevention requires preventing the progression of DR in patients with DR by continuing systemic risk factor control, regular screening to monitor for the progression of mild DR to vision-threatening stages, and the development and implementation of evidence-based guidelines for managing DR. In this aspect, telemedicine-based DR screening incorporating artificial intelligence technology has the potential to facilitate more widespread and cost-effective screening, particularly in low- and middle-income countries. Tertiary prevention of DR blindness has been the main focus of the clinical ophthalmology community, classically based on laser photocoagulation treatment and ocular surgery but with an increasing use of anti-vascular endothelial growth factor (anti-VEGF) for vision-threatening DR. Evidence from serial epidemiological studies shows blindness due to DR has declined in high-income countries (e.g., the USA and UK) due to coordinated public health education efforts, increased awareness, early detection by DR screening, sustained systemic risk factor control, and the availability of effective tertiary level treatment. However, the progress made in reducing DR blindness in high-income countries may be overwhelmed by the increasing numbers of patients with diabetes and DR in low- and middle-income countries (e.g., China, India, Indonesia, etc.).

Artificial intelligence (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds (1). Not surprisingly, most AI developments in health care cater to the needs of high-income countries (HICs), where the majority of research is conducted. Conversely, little is discussed about what AI can bring to medical practice in low- and middle-income countries (LMICs), where workforce shortages and limited resources constrain the access to and quality of care. AI could play an important role in addressing global health care inequities at the individual patient, health system, and population levels.