NEWS AND VIEWS29 April 2026A machine-learning tool that allocates scarce medicines to meet demand and reduce waste is providing millions with better health care as it rolls out nationwide.ByZiad Obermeyer0Ziad ObermeyerZiad Obermeyer is in the School of Public Health, University of California, Berkeley, Berkeley, California 94720, USA.View author publicationsSearch author on: PubMed Google ScholarScarcity shapes nearly every aspect of health-care delivery in low- and middle-income countries (LMICs), so making good decisions about how to allocate resources is crucial. Yet the tools used to guide these choices are themselves scarce. Methods for forecasting demand, coordinating supply chains and distributing resources, which are taken for granted in high-income settings, are often unavailable or unreliable in LMICs. Writing in Nature, Chung et al.1 describe a machine-learning system for allocating scarce medicines. It works by forecasting demand for medical supplies — how much medication will be consumed, in what clinic, on what day — and using these predictions to guide distribution decisions. Rather than simply offering an algorithmic proof-of-concept, the paper reports a decision engine that is designed for and implemented in the messiness of real-world health systems.Access optionsAccess Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription27,99 € / 30 dayscancel any timeLearn moreSubscribe to this journalReceive 51 print issues and online access199,00 € per yearonly 3,90 € per issueLearn moreRent or buy this articlePrices vary by article typefrom$1.95to$39.95Learn morePrices may be subject to local taxes which are calculated during checkoutdoi: https://doi.org/10.1038/d41586-026-01152-0ReferencesChung, A. T.-H. et al. Nature https://doi.org/10.1038/s41586-026-10433-7 (2026).Article Google Scholar Glickman, S. W. et al. N. Engl. J. Med. 19, 816–823 (2009).Article Google Scholar Ruamviboonsuk, P. et al. Lancet Digit. Health 4, 235–244 (2022).Article Google Scholar Nisingizwe, M. P. et al. Lancet Glob. Health 10, 564–569 (2022).Article Google Scholar Download referencesCompeting InterestsThe author declares no competing interests. Read the paper: Improving access to essential medicines via decision-aware machine learning Mobile delivery of COVID-19 vaccines improved uptake in rural Sierra Leone AI uses patient data to optimize selection of eligibility criteria for clinical trialsSee all News & ViewsSubjectsHealth careMachine learningDeveloping worldLatest on:Health careMachine learningDeveloping worldJobs Assistant Professor, Stanford DermatologyThe Department of Dermatology at Stanford University is seeking an Assistant Professor...Stanford, California (US)Stanford DermatologyPostdoc in Computational BiologyPostdoc in Computational Biology | Human Technopole, Milan Build the science that shapes the future of human health. Application closing date: 20.0...Milan (IT)Human TechnopolePostdoctoral Associate: Unsupervised Learning for DNA/RNA Molecular DynamicsInterpretable DNA/RNA ensemble quantification with molecular dynamics, machine learning, clustering, and measurement analysis.Gaithersburg, MarylandBiophysical and Biomedical Measurement Group, National Institute of Standards and TechnologyAssociate or Senior Editor, Communications AI & ComputingJob Title: Associate or Senior Editor, Communications AI & Computing Locations: Shanghai, Beijing, Pune or New Delhi (hybrid) Application deadline:...Shanghai, Beijing, Pune or New Delhi (hybrid)Springer Nature Ltd