Colin Vickrey ’27
Each year, nearly half the world’s population lives without access to essential health services. In rural regions, diagnostic delays can turn treatable disease into deadly ones. According to a 2022 Lancet Digital Health report, more than 3 billion people live in areas without adequate diagnostic imaging or laboratory infrastructure. In many of these communities, patients must travel hours, or even days, to reach a hospital equipped with specialists. Thus turning preventable diseases such as pneumonia or tuberculosis into fatal ones.
Tuberculosis (TB) remains a leading infectious cause of death worldwide, particularly in low-resource settings where radiologists are scarce. Chest X-rays are a cornerstone of TB detection, but human interpretation is often unavailable or inconsistent. A comparative study published by Nature Scientific Reports evaluated three AI systems, CAD4TB, Lunit INSIGHT, and qXR, on chest radiographs from Nepal and Cameroon. Each achieved area-under-the-curve (AUC) values near 0.94, performing on par with expert radiologists and, in some cases, exceeding their specificity when sensitivity was matched (Murphy et al., 2019). A separate study by Google Health found similar results, with a deep-learning model detecting TB on chest X-rays with accuracy comparable to radiologists across diverse international datasets (Lakhani and Sundaram, 2017).
These technologies are increasingly being deployed beyond the lab. Ultra-portable digital X-ray devices paired with AI interpretation are now used in mobile clinics in sub-Saharan Africa and South Asia. Programs supported by Unitaid have introduced these units in rural communities, allowing health workers to identify probable TB cases for confirmatory molecular testing within minutes (Unitaid, 2023). Early evaluations in NEJM AI have confirmed that AI-assisted screening can reliably triage patients for follow-up testing, even in field environments with limited infrastructure (NEJM AI, 2024).
While using AI takes the trained radiologist out of the equation, AI diagnostic tools are only as effective as the infrastructure supporting them. In a recent review the deployment of AI solutions in rural and underserved regions showed to be often limited by unstable internet, inadequate diagnostic equipment, and insufficient technical support (Balakrishnan et al., 2025). Thereby limiting the consistent use of even portable or offline AI systems. The World Health Organization’s Global Observatory for eHealth (2023) found that fewer than half of rural health facilities in sub-Saharan Africa have continuous power supply, and only about one-third have broadband access.
These limitations reveal that infrastructure, not algorithmic capability, is often the rate-limiting factor in bringing AI diagnostics to underserved regions. Without investments in power, connectivity, and local capacity, even the most advanced tools cannot close the healthcare access gap.
Artificial intelligence has demonstrated remarkable potential to extend diagnostic capabilities to regions long underserved by modern healthcare. From detecting tuberculosis on portable chest X-rays to triaging patients in mobile clinics, AI offers a pathway to bring specialist-level interpretation directly to the point of care. Yet the effectiveness of these tools depends not only on algorithmic accuracy but also on the systems that sustain them. Without reliable infrastructure such as electricity, connectivity, and trained personnel, the promise of AI will remain unevenly distributed.
To truly bridge the diagnostic divide, innovation must be paired with investment in the foundations of care. Strengthening rural infrastructure and ensuring equitable deployment will determine whether AI becomes another symbol of technological disparity or a genuine step toward global health equity. In this balance lies the future of accessible medicine, helping close the distance of everyone receiving life-saving diagnoses.
Colin Vickrey is a staff writer at The Princeton Medical Review. He can be reached at cv0191@princeton.edu.
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