Mobile CT Services for Rural, Regional, and Remote Areas: Current Practice and Future Integration with Telehealth and Regulatory-Authorised AI

Access to advanced medical imaging like computed tomography (CT) is often limited in rural, regional, and remote (RRR) areas due to high infrastructure costs and a shortage of specialized personnel. This review evaluates the potential of three complementary technologies—mobile CT platforms, telehealth, and regulatory-authorized artificial intelligence (AI)—to bridge this gap. The authors examine how these tools, when combined, could create a connected clinical pathway that brings diagnostic services directly to underserved populations.

The study finds that each technology has reached a high level of maturity independently. Mobile CT units (trucks, trailers, and specialized stroke ambulances) are operational globally, with many programs achieving high technology readiness levels (TRL). Telehealth is a well-established standard for remote radiology reporting and specialist consultation. Similarly, numerous AI-enabled CT software tools have received regulatory authorization (e.g., FDA, TGA) and are being deployed for tasks like stroke triage, lung nodule detection, and radiotherapy planning.

Despite the maturity of individual components, the paper highlights a significant "integration gap." Most current deployments use these technologies in isolation or in simple pairs (e.g., mobile CT with remote reporting). The authors identify only a few examples of full three-way integration, such as mobile lung cancer screening programs in Western China that combine mobile image acquisition, remote specialist review, and AI-assisted analysis. The authors argue that simply deploying these tools is not enough; they must be validated as a complete, interoperable system to ensure safety and clinical efficacy.

To move forward, the authors call for prospective, multicenter studies that evaluate integrated mobile CT pathways in real-world RRR settings. Future research must prioritize addressing "domain shift"—where AI models trained on hospital-grade scanners fail to perform accurately on mobile platforms—as well as ensuring robust cybersecurity, reliable connectivity, and clear clinical governance. The ultimate goal is to transform these mature individual technologies into sustainable, scalable services that provide equitable care.