Computed tomography (CT) is one of the most valuable imaging tools in clinical medicine, yet conventional CT systems remain costly, complex, and dependent on specialized personnel. A typical multi-slice CT scanner requires an investment of $1–$2 million, along with annual service contracts that often exceed $100,000. These systems also demand radiation shielding, continuous maintenance, tube replacements, and highly trained technologists and radiologists. While large hospitals can sustain this operational burden, many small clinics, long-term care facilities, and rural hospitals cannot. As a result, older adults and medically fragile patients are frequently required to travel long distances to regional centers for imaging, exposing them to pain, fatigue, delirium, infection, and substantial logistical challenges.

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For elderly and palliative care populations, the clinical questions differ sharply from those in high-acuity settings. Near the end of life, clinicians rarely need high-precision, multi-phase CT protocols. Instead, they need clear, straightforward information: Is there a large pleural effusion? Is the bowel obstructed? Is there urinary retention, ascites, or a displaced fracture? These findings strongly influence comfort-oriented management. When transporting frail or terminally ill patients imposes more harm than benefit, a simpler, more accessible form of CT becomes essential.

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Emerging trends in low-cost imaging—especially those derived from dental cone-beam CT (CBCT)—point to a feasible alternative. Dental CBCT demonstrates that many high-cost components of hospital-grade CT systems can be replaced with low-speed gantries, compact mechanical structures, lightweight shielding, and dose-efficient acquisition. By extending CBCT principles to torso or whole-body configurations, it becomes possible to detect major abnormalities at a fraction of the cost and mechanical complexity of conventional CT. Although such systems cannot match the diagnostic precision of high-end scanners—owing to factors such as motion artifacts, scatter, and limited angular sampling—they can still meet the clinical needs of comfort-focused care, where the priority is clarity rather than exhaustive detail.

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Artificial intelligence plays a central enabling role in this reconfigured model of community imaging. AI-assisted acquisition guidance can help general physicians or trained nurses position the scanner, select appropriate parameters, identify motion or misalignment, and receive real-time corrective feedback. AI-based reconstruction can compensate for low-dose data, limited-angle scans, mechanical variability, or incomplete trajectories—allowing lower-cost hardware to produce clinically meaningful images. Automated AI summaries can highlight large effusions, gross fractures, obstructive patterns, or other urgent findings, reducing the interpretive burden and integrating seamlessly with remote tele-radiology when specialist input is required.

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Together, these developments create a pathway for imaging that is inexpensive, easy to maintain, and operable by general physicians in settings where traditional CT is impractical. By lowering cost, simplifying installation, reducing dependence on specialized technologists, and incorporating AI-driven support, full-body CT can move from centralized hospitals to community clinics, long-term care facilities, and even bedside environments. Such a shift aligns imaging capabilities with the actual needs of elderly and palliative populations—providing clear, actionable information without imposing unnecessary physical, financial, or psychological burden.