A Queensland research team developed a machine‑learning tool that mapped the prevalence and networks of fraudulent papers—so‑called paper mills—affecting the cancer literature. The model identified clusters of suspicious articles, authorship patterns, and reused imagery, quantifying the reach of low‑quality or fabricated publications that can distort the scientific record. By providing scalable detection, the tool offers publishers, institutions, and funders a data‑driven way to prioritize investigations and retract problematic studies. Cancer researchers and journals may need to adopt similar automated screening to safeguard reproducibility, protect clinical translation pipelines, and limit downstream impacts on drug development decisions.