The AI-POCUS Evidence Hub is an LMIC-anchored infrastructure initiative for the responsible development, validation, synthesis, and translation of AI-enabled point-of-care ultrasound in low-resource and humanitarian health systems.
The Hub supports a multi-country Community of Practice working to make AI-POCUS evidence more standardised, interoperable, reusable, and policy-ready.
AI-POCUS can extend diagnostic capacity beyond hospitals and specialist services by combining portable ultrasound with automated acquisition support, scan quality assessment, image interpretation, and clinical decision guidance.
This organization hosts shared resources for AI-POCUS research, evidence synthesis, validation, and implementation across priority use cases including infectious disease, maternal health, emergency care, and primary care.
This Hugging Face organization will host and maintain:
Initial priority areas include:
The Hub is designed to address fragmentation in the AI-POCUS evidence base by supporting shared standards for:
These resources are intended to make AI-POCUS studies easier to compare, validate, synthesize, and translate into policy and implementation guidance.
The Hub supports responsible, tiered access to AI-POCUS research outputs.
Open resources may include dataset documentation, metadata templates, protocols, evidence maps, model cards, code, and training materials.
Access to patient-level ultrasound imaging or sensitive clinical metadata may be restricted and governed by local ethics approvals, data use agreements, institutional review, and partner requirements. Where possible, the Hub supports federated validation and benchmarking without requiring raw clinical data to leave originating institutions.
The Hub is connected to a multi-country AI-POCUS Community of Practice involving researchers, clinicians, implementers, data scientists, policymakers, evidence synthesis experts, and global health partners.
Working areas include:
The Hub prioritises LMIC leadership, equitable governance, open science, responsible AI, FAIR data principles, and policy-oriented evidence generation.
Each repository will include its own documentation, citation guidance, license terms, and access conditions.
If you use Hub resources, please cite the relevant dataset, model, protocol, evidence map, or synthesis product using the citation information provided in the corresponding repository.
For collaboration, dataset contribution, governance, or Community of Practice enquiries:
EPFL LiGHT Laboratory
Email: mary-anne.hartley@epfl.ch
Website: AI-POCUS Community of Practice
Unless otherwise specified, code and documentation will be released under permissive open-source licenses such as MIT or Apache 2.0.
Dataset access and reuse terms will be specified individually according to consent, ethics approvals, data use agreements, and partner governance requirements.