JMIR Publications

ABSTRACT

Background:

Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist, particularly in rural settings. Artificial intelligence (AI)-enhanced predictive analytics theoretically offer opportunities for optimizing tele-oncology deployment through personalized, data-driven strategies. However, current evidence on AI applications in rural tele-oncology contexts remains limited, and critical equity considerations remain underexamined.

Objective:

This scoping review mapped evidence on AI-enhanced predictive analytics in tele-oncology implementation, with particular attention to rural and underserved populations, to identify research gaps and inform implementation science priorities.

Methods:

We searched five databases (PubMed, Embase, CINAHL, Web of Science, IEEE Xplore) using four concept domains (tele-oncology, rural implementation barriers, AI/predictive analytics, implementation science) from January 2015 through April 2025. Following an initial search completed April 9, 2025, we conducted an expanded search in November 2025 after reviewer feedback indicated limited evidence. Two independent reviewers screened 330 unique records (title/abstract Cohen's κ=0.78), with the principal investigator resolving conflicts. Of 138 full-text reviews (κ=0.82), 6 studies met inclusion criteria. Data extraction captured study characteristics, AI applications, implementation factors, and outcomes. We employed narrative thematic analysis to map findings into three themes: (1) current tele-oncology implementation landscape in rural/underserved settings, (2) potential AI applications addressing implementation challenges, and (3) implementation considerations for AI systems themselves.

Results:

Six included studies (2 systematic reviews, 2 pilot feasibility studies, 1 cross-sectional predictive study, 1 platform development study; published 2019-2025) demonstrated limited evidence at the intersection of AI, tele-oncology, and rural health equity. Khairat et al. found patient characteristics predicted telehealth modality preferences with 86.2% accuracy, revealing that male patients exhibited 66% increased odds of video selection versus female patients (p=.004), and urban residents showed 101% increased odds compared to rural counterparts (p<.001). Liu et al. demonstrated that disadvantaged populations engaged with AI-generated health literacy content 2.52-fold more frequently than non-disadvantaged counterparts. However, all six studies documented substantial implementation barriers (patient, provider, organizational, system levels) persisting despite technological sophistication. Daly et al. identified organizational threshold effects, where remote monitoring interventions succeeded with adequate provider capacity but failed under resource constraints—suggesting that algorithmic innovations cannot overcome structural limitations in rural facilities. No studies explicitly examined algorithmic bias, cross-population validation, or potential harms in rural contexts. Geographic concentration in high-resource countries (United States n=6, Greece n=1, Australia n=1) and limited oncology-specific focus (37.5%) underscore structural gaps in knowledge generation for underserved populations.

Conclusions:

Current evidence remains insufficient to support definitive practice recommendations. The profound evidence gap reflects structural inequities in knowledge generation: populations with greatest implementation challenges remain dramatically underrepresented in AI and digital health literature. Future research should prioritize comparative effectiveness studies in authentic rural contexts with implementation science outcomes, equity-centered cross-population validation, specification of translation mechanisms linking AI predictions to implementation strategies, health economic analyses, and mechanistic research on sociotechnical integration factors, ensuring technological innovation reduces rather than perpetuates cancer care disparities.