Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology.

Scarce or absent radiology resources impede adoption of artificial intelligence (AI) for medical imaging by resource-poor health institutions. They face limitations in local equipment, personnel expertise, infrastructure, data-rights frameworks, and public policies. The trustworthiness of AI for medical decision making in global health and low-resource settings is hampered by insufficient data diversity, nontransparent AI algorithms, and resource-poor health institutions' limited participation in AI production and validation. RAD-AID's three-pronged integrated strategy for AI adoption in resource-poor health institutions is presented, which includes clinical radiology education, infrastructure implementation, and phased AI introduction. This strategy derives from RAD-AID's more-than-a-decade experience as a nonprofit organization developing radiology in resource-poor health institutions, both in the United States and in low- and middle-income countries. The three components synergistically provide the foundation to address health care disparities. Local radiology personnel expertise is augmented through comprehensive education. Software, hardware, and radiologic and networking infrastructure enables radiology workflows incorporating AI. These educational and infrastructure developments occur while RAD-AID delivers phased introduction, testing, and scaling of AI via global health collaborations.

Overcoming Challenges for Successful PACS Installation in Low-Resource Regions: Our Experience in Nigeria.

In this paper, we walk you through our challenges, successes, and experience while participating in a Global Health Outreach Project at the University College Hospital (UCH) Ibadan, Nigeria. The scope of the project was to install a Picture Archive and Communication System (PACS) to establish a centralized viewing network at UCH's Radiology Department, for each of their digital modalities. Installing a PACS requires robust servers, the ability to retrieve and archive studies, ensuring workstations can view studies, and the configuration of imaging modalities to send studies. We anticipated that we might experience hurdles for each of these requirements, due to limited resources and without the availability to make a site visit prior to the start of the project. While we ultimately experienced delays and troubleshooting was required at each turn of the install, with the help of dedicated volunteers both on and off-site and the UCH staff, our shared goal was accomplished.

Building Radiology Equity: Themes from the 2023 RAD-AID Conference on International Radiology and Global Health.

Low- and middle-income countries are significantly impacted by the global scarcity of medical imaging services. Medical imaging is an essential component for diagnosis and guided treatment, which is needed to meet the current challenges of increasing chronic diseases and preparedness for acute-care response. We present some key themes essential for improving global health equity, which were discussed at the 2023 RAD-AID Conference on International Radiology and Global Health. They include (1) capacity building, (2) artificial intelligence, (3) community-based patient navigation, (4) organizational design for multidisciplinary global health strategy, (5) implementation science, and (6) innovation. Although not exhaustive, these themes should be considered influential as we guide and expand global health radiology programs in low- and middle-income countries in the coming years.

Deploying Artificial Intelligence for Thoracic Imaging Around the World.

PURPOSE: Artificial intelligence (AI) thoracic imaging applications are increasingly being deployed in low- and middle-income countries (LMICs). Radiologists have a critical gatekeeping role to ensure the effective and ethical implementation of AI solutions. RAD-AID International uses a three-pronged implementation strategy to overcome challenges pervasive in LMICs. METHODS: During a similar period, an AI software for chest radiography (CXR) interpretation was deployed at two tertiary hospitals located in Guyana and Nigeria. The three-pronged implementation strategy of clinical education, infrastructure implementation, and phased AI introduction was used. A PACS with a cloud component was installed at each institution. Radiology residents and attending physicians at these institutions completed an introduction-to-AI course to prime them for the use of AI solutions. A phased introduction of the AI software was performed to allow local validation as well as trust building and workflow integration. Local validation processes were used at each site by comparing AI outputs with standardized prospectively generated reports by local radiologists and study team members, allowing for slight differences in the goals of AI software use between sites. RESULTS: The PACS was successfully installed at both institutions. Thirty participants completed the introduction-to-AI course with an average pre-knowledge test score of 75% and an average posttest score of 95%. The focus of the validation process at various sites was reflective of the intended use of the AI software. In Guyana, it revealed an 87% concordance rate between radiologists and the AI model for triaging normal versus abnormal findings on CXR. In Nigeria, an 85% concordance rate between radiologists and the AI model for reporting tuberculosis on CXR was noted. The AI software was successfully deployed and is being used as intended at both institutions. CONCLUSIONS: There are unique barriers to the adoption of AI in LMICs requiring an implementation strategy in collaboration with local institutions and industry partners to ensure success.