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

Area of Science:

• Digital Health
• Oncology
• Implementation Science
• Artificial Intelligence

Background:

• Tele-oncology aims to reduce geographic barriers in cancer care, but rural implementation faces significant challenges.
• AI-enhanced predictive analytics offer personalized strategies but lack robust evidence in rural settings, with underexplored equity considerations.

Purpose of the Study:

• To map evidence on AI-enhanced predictive analytics in tele-oncology implementation, focusing on rural and underserved populations.
• To identify research gaps and inform implementation science priorities for AI in rural tele-oncology.

Main Methods:

• A scoping review searched 5 databases (PubMed, Embase, CINAHL, Web of Science, IEEE Xplore) from January 2015 to November 2025.
• Included 4 studies meeting criteria after screening 330 unique records and 138 full-text reviews.
• Employed narrative thematic analysis to identify themes related to AI, tele-oncology, rural settings, and implementation.

Main Results:

• Limited evidence exists at the intersection of AI, tele-oncology, and rural health equity.
• AI demonstrated potential in predicting telehealth preferences and engaging disadvantaged populations with health literacy content.
• Substantial implementation barriers (patient, provider, organizational, system levels) persist, indicating AI cannot overcome structural limitations.

Conclusions:

• Current evidence is insufficient for practice recommendations due to significant research gaps and underrepresentation of underserved populations.
• Future research should prioritize comparative effectiveness, equity-centered validation, and health economic analyses in authentic rural contexts.
• Ensuring AI reduces, rather than perpetuates, cancer care disparities requires focused research on sociotechnical integration and implementation science outcomes.