AI-Enhanced Predictive Analytics to Optimize Tele-Oncology Implementation in Rural Settings: Scoping Review.

Background: Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist in rural settings. AI-enhanced predictive analytics offer opportunities for optimizing deployment through personalized, data-driven strategies; however, evidence in rural tele-oncology contexts remains limited, and critical equity considerations remain underexamined. Objective: This scoping review aimed to map 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 5 databases (PubMed, Embase, CINAHL, Web of Science, and IEEE Xplore) using 4 concept domains (tele-oncology, rural implementation barriers, AI or predictive analytics, implementation science) from January 2015 through November 2025. Two independent reviewers screened 330 unique records (title or abstract; Cohen κ=0.78), with the principal investigator resolving conflicts. Of 138 full-text reviews (κ=0.82), 4 studies met inclusion criteria. Data extraction captured study characteristics, AI applications, implementation factors, and outcomes. We used narrative thematic analysis to map findings into three themes: (1) the current tele-oncology implementation landscape in rural and underserved settings, (2) potential AI applications addressing implementation challenges, and (3) implementation considerations for AI systems themselves. Results: Four included studies (1 pilot feasibility study, 1 proof-of-concept validation study, 1 cross-sectional predictive study, and 1 platform development study; published 2019-2025) demonstrated limited evidence at the intersection of AI, tele-oncology, and rural health equity. 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 nondisadvantaged counterparts. However, all 4 studies documented substantial implementation barriers (patient, provider, organizational, and system levels) persisting despite technological sophistication. Organizational threshold effects, where remote monitoring interventions succeeded with adequate provider capacity but failed under resource constraints-suggest 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=2, Greece n=1, and Singapore n=1) and limited oncology-specific focus underscore structural gaps in knowledge generation for underserved populations. Conclusions: Current evidence remains insufficient to support definitive practice recommendations. The observed evidence gap may reflect broader structural inequities in knowledge generation: populations with the greatest implementation challenges appear to remain substantially 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 disparities in cancer care.