Toward AI‐Assisted Methodological Evaluations of Health Risk Analyses Using Diverse Types of Evidence

ABSTRACT How should the credibility of claims about the human health effects of exposures be evaluated when their support consists of multiple individually inconclusive lines of evidence? Health risk analysis typically draws on heterogeneous evidence streams—including observational epidemiology, systematic reviews and meta‐analyses, mechanistic and toxicological evidence, and policy‐facing syntheses—to support causal conclusions and risk management recommendations. However, important distinctions among observed associations, causal interpretations, mechanistic plausibility, and claims about intervention or policy effectiveness are often blurred in scientific reviews and evidence syntheses, making evaluations difficult to reproduce, audit, or systematically critique. This article introduces ASP1 (Automated Scientific Paper reviewer 1), available online at https://moirai.shinyapps.io/asp1_pdf_runner/ , an AI‐assisted framework for systematic, transparent evaluation of heterogeneous scientific evidence and of the conclusions drawn from it. ASP1 combines modular review components, structured claim extraction, explicit linkage between evaluative judgments and supporting text, and cross‐module synthesis to expose and systematically examine the evidentiary and inferential chain connecting a document's evidence to its conclusions. We illustrate it using a mini‐corpus of recent publications concerning ultra‐processed foods (UPFs) and health spanning prospective cohort studies, case–control studies, umbrella reviews, mechanistic syntheses, and policy‐facing evidence reviews. Results can be browsed at https://moirai.shinyapps.io/asp1_bundle_browser/ . Across these diverse study designs, ASP1 consistently distinguished evidence of association from stronger causal and interventional claims and identified important limitations involving residual confounding, exposure misclassification, mechanistic uncertainty, category heterogeneity, and overextension of policy conclusions beyond direct evidentiary support. Comparison with independent expert human commentary on a recent Lancet review showed substantial overlap in major inferential concerns identified by ASP1 and by skilled human reviewers, while also revealing complementary strengths. ASP1 was generally stronger in explicit inferential decomposition, claim‐specific support classification, consistency, and auditability; human experts contributed richer contextual understanding, sharper methodological intuition, and more vivid and concrete examples of evidentiary limitations. These results suggest that current AI systems guided by structured evaluative frameworks can support more transparent, reproducible, and disciplined review of heterogeneous scientific evidence at realistic scales. The greatest promise of such systems may lie less in automating scientific judgment than in helping make scientific reasoning more explicit, inspectable, auditable, and open to challenge.