Artificial Intelligence for Radon Anomalies as Earthquake Precursors: A Systematic Review of Methods and Performance

Radon has long been investigated as a potential earthquake precursor, yet its interpretation remains challenged by meteorological, hydrological, and instrumental variability that can generate apparent departures unrelated to tectonic processes. This review synthesises how artificial intelligence is being applied in radon-based earthquake precursor research, with particular emphasis on anomaly detection and the evaluation of radon seismicity associations. Following a PRISMA-guided workflow, Scopus and the Web of Science Core Collection are searched and screened for eligibility, yielding 26 journal articles, most of which are concentrated in a limited number of tectonically active regions. Across the reviewed literature, a consistent pattern emerges: AI is used primarily to model the expected radon background, while candidate precursors are identified mainly through threshold-based indices derived from residuals or concentration ratios rather than through explicit earthquake-probability outputs. Although pre-seismic departures are reported repeatedly, this review shows that the evidence base remains constrained by heterogeneous operational definitions of anomaly, strong methodological variation across studies, a predominant emphasis on background goodness-of-fit instead of alarm-level performance, and limited use of time-ordered validation. These findings highlight both the promise and the current limitations of AI-enabled radon analysis. The main contribution of the field so far is not direct earthquake prediction but a more structured framework for separating potential tectonic signals from non-seismic variability. In this sense, the review provides an important methodological synthesis for future research and shows that more reproducible and operationally useful radon monitoring will depend on clearer anomaly definitions, stronger confounder control, more rigorous temporal validation, and more standardised performance reporting.