Improving Point-of-Care Diagnostics in Emergency and Rural Environments with Portable and Handheld Imaging Devices

Abstract

Portable point-of-care ultrasound, X-ray, and new MRI and CT modalities have revolutionized point-of-care diagnosis in emergency and rural settings. This review embraces their use with telemedicine and artificial intelligence (AI) and their revolutionary impact on health care delivery in resource-poor settings. Tele-radiology enables remote image interpretation, reducing diagnostic delays by as much as 40% in LMIC settings and disaster scenarios, as AI algorithms improve diagnostic accuracy, detecting pneumothorax with 92% sensitivity. These applications have overcome basic barriers of specialist-to-remote-location radiologist and infrastructure deficits by allowing non-specialists to acquire and interpret studied case images. Case studies from rural Africa, India, and disaster environments illustrate their impact on increased access, turnaround time, and patient outcomes of tuberculosis and trauma. Limited connectivity, bandwidth cost, and AI algorithm bias, however, are major challenges and must be addressed by low-bandwidth compression and validation on disparate datasets. By combining portability with cutting-edge digital tools, these devices provide a scalable solution to global health inequity, with a continued need, nonetheless, for investments in infrastructure and training.