Tech That Scales: A Practical Framework for Artificial Intelligence-Enabled Cancer Care in Low- and Middle-Income Countries and Underserved US Counties

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - framework development study

• Primary Outcome: Four-layer technology framework for scalable cancer care in resource-limited settings

• Nnt Or Sensitivity: Framework includes equity monitoring metrics and QA frameworks for implementation assessment

Clinical Application

Moderate - requires significant infrastructure investment and local adaptation, but provides practical implementation guidance Relevant for rural and remote Australian cancer services, Aboriginal and Torres Strait Islander health programs, and telehealth initiatives supported by Medicare Benefits Schedule Cancer care programs in LMICs and underserved regions with limited specialist workforce and infrastructure

Abstract

Cancer outcomes remain starkly unequal: 5-year survival rates for common malignancies in low- and middle-income countries (LMICs) often lag 20-40 percentage points behind high-income benchmarks, and similar disparities persist between well-resourced metropolitan centers and rural or safety-net settings within high-income countries. The gap is driven less by the absence of effective interventions than by workforce shortages, fragmented referral pathways, limited infrastructure, and loss to follow-up after abnormal screens. Technologies that scale must therefore function as deployable workflows integrating staffing, logistics, quality assurance (QA), governance, and monitoring not merely as stand-alone algorithms or devices.1 This review synthesizes evidence across four complementary technology families that address these constraints across the continuum of care: artificial intelligence (AI)-supported screening and triage as the population entry-point layer; Project ECHO telementoring as the workforce-capacity layer; electronic patient-reported outcomes and remote symptom monitoring as the longitudinal continuity layer; and AI-powered clinical trial prescreening hubs as the access-to-innovation layer. The technologies do not carry equal evidentiary weight, and they should not be deployed identically in every setting. Our aim is to show how oncology leaders can sequence them pragmatically inside a common operating logic while adapting to local infrastructure and governance. We organize the framework in patient journey order entry into care through screening, workforce support through telementoring, continuity through remote monitoring, and access to innovation through trial prescreening, and draw examples from both LMIC programs and underserved US settings. We therefore present a practical implementation playbook, including a 90-day launch checklist, staffing models, QA frameworks, equity and bias monitoring metrics, and a program outcomes dashboard designed for oncology leaders seeking to move beyond pilots toward durable, monitored deployment.

References

1. 1.Loaiza-Bonilla, A., Basu, P., Lucas, E., Yost, C., & Arora, S. (2026). Tech that scales: A practical framework for artificial intelligence-enabled cancer care in low- and middle-income countries and underserved US counties. American Society of Clinical Oncology Educational Book. https://doi.org/10.1200/EDBK-26-521200