Artificial intelligence to enhance research integrity in evidence-based medicine: toward scalable trustworthiness assessment.

Fraudulent, falsified, and otherwise unreliable clinical research threatens the foundations of evidence-based medicine because systematic reviews, meta-analyses, and clinical practice guidelines depend on the trustworthiness of the studies they include. Retractions are increasing faster than publication output and often occur only after substantial delay, allowing problematic trials to be cited, pooled, and incorporated into downstream clinical recommendations. Recent evidence shows that retracted randomized trials have contaminated thousands of meta-analyses and hundreds of guideline documents, while only a small minority of affected reviews later self-correct. This is not only a clinical problem but also an integrity analytics problem: unreliable studies generate article-level, trial-level, and network-level signals that are not yet systematically integrated into evidence-synthesis workflows. Existing approaches can detect some image anomalies, textual irregularities, reporting inconsistencies, and statistical red flags, but important blind spots remain. Fabricated clinical datasets may appear statistically plausible, outcome switching often requires registration-publication comparison, and most available tools operate in isolation rather than as coordinated screening systems. We argue that the next generation of safeguards should combine AI/ML-based detection systems, statistical forensic methods, structured trustworthiness appraisal tools, and workflow-level screening frameworks within evidence synthesis. Embedding staged trustworthiness assessment early in systematic reviews, alongside stronger publisher- and database-level integrity infrastructure, could help prevent unreliable trials from distorting pooled estimates and downstream guidance. Such systems should support triage and prioritization rather than replace human judgment. Framed in this way, scalable trustworthiness assessment represents a practical research metrics and analytics agenda for strengthening the evidence ecosystem from primary studies to reviews, guidelines, and policy decisions.