As we look back on 2025, the world experienced a year of both remarkable achievement and profound challenge in global health. Multilateralism, science and solidarity were tested as never before, underscoring a fundamental truth: International cooperation is not optional. It is essential if we are to protect and promote health for everyone, everywhere in 2026 and beyond. Perhaps the most significant milestone was the adoption by WHO Member States of the Pandemic Agreement, a landmark step towards making the world safer from future pandemics. Alongside this, amendments to the International Health Regulations came into force, including a new "pandemic emergency" alert level designed to trigger stronger global cooperation.

With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.

With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health.

Mobile health apps provide a unique means of collecting data that can be used to deliver adaptive interventions.The predicted outcomes considerably influence the selection of such interventions. Recourse via counterfactuals provides tangible mechanisms to modify user predictions. By identifying plausible actions that increase the likelihood of a desired prediction, stakeholders are afforded agency over their predictions. Furthermore, recourse mechanisms enable counterfactual reasoning that can help provide insights into candidates for causal interventional features. We demonstrate the feasibility of GAN-generated recourse for mobile health applications on ensemble-survival-analysis-based prediction of medium-term engagement in the Safe Delivery App, a digital training tool for skilled birth attendants.

Language is a valuable source of clinical information in Alzheimer's Disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. This paper summarises current findings on the use of artificial intelligence, speech and language processing to predict cognitive decline in the context of Alzheimer's Disease, detailing current research procedures, highlighting their limitations and suggesting strategies to address them. We conducted a systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase) and Web of Science. Bibliographies of relevant papers were screened until December 2019. From 3,654 search results 51 articles were selected against the eligibility criteria. Four tables summarise their findings: study details (aim, population, interventions, comparisons, methods and outcomes), data details (size, type, modalities, annotation, balance, availability and language of study), methodology (pre-processing, feature generation, machine learning, evaluation and results) and clinical applicability (research implications, clinical potential, risk of bias and strengths/limitations). While promising results are reported across nearly all 51 studies, very few have been implemented in clinical research or practice. We concluded that the main limitations of the field are poor standardisation, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Attempts to close these gaps should support translation of future research into clinical practice.

The virtual International Conference on Learning Representations (ICLR) was held on 26-30 April and included eight keynote talks, with a wide range of topics covered. Courtesy of the conference organisers you can watch the talks in full and see the question and answer sessions too. Africa has a population of over one billion people, over 3000 ethnic groups, and over 2000 different languages. This rich diversity offers an excellent opportunity to address complex research questions within the African continent. Research in Africa within the AI space can have global impact.

Despite huge advancements and progress in the world of global health over the past decades, many middle and low-income countries are still falling behind, unable to reach their sustainable development goals. This, in turn, is creating an urgency to prioritize wellbeing, and AI holds enormous promise in transforming the provision of healthcare in resource strained environments. The Artificial Intelligence in Global Health report, funded by the USAID's Center for Innovation and Impact, Rockefeller Foundation, and the Bill & Melinda Gates Foundation, outlined 27 cases of AI in global healthcare, and the massive potential it holds for drastically improving health in LEDC's. The use of AI was split into four key areas - population health, patient and front line health worker virtual assistants, and physician clinical decision support. Not only does the report provide solutions that could improve the access, quality, and effectiveness of global healthcare, but it also takes into account the current maturity of AI systems and the feasibility of these solutions.

"It's about saving as many lives as we possibly can," Tim Wood said. Wood spoke to Industrious en route to a meeting with USAID about its Global Health Supply Chain Program-Procurement and Supply Management project, implemented by Chemonics, a development contractor, and a consortium of partners, including IBM. Getting bed nets, HIV medication and other health supplies from medical storage facilities in Washington DC to remote parts of Africa is no small feat. But Wood, a global supply chain VP at IBM, and his GHSC-PSM consortium partners are doing just that. Global supply chains are crucial to any business or operation.

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.