Expertise in AI and clinical publishing exposes peer review gaps: A perspective

Abstract

Artificial intelligence and machine learning are advancing rapidly in medical and mental health research, yet clinical publishing remains structurally unprepared to evaluate these technologies with the rigor they demand. Despite the rise of AI-driven models for suicide risk prediction and diagnostic assessment, editorial and peer review processes often lack the technical expertise required to assess methodological validity. Drawing on dual fluency in AI and clinical publishing, this perspective identifies a critical gap at the intersection of innovation and editorial oversight. This article reveals how editorial decisions in high-impact psychiatry journals have dismissed valid methodological concerns as “overly technical” and undermined independent scientific critique, drawing on two case studies: one involving a model that differentiates suicidal from non-suicidal self-harm, and another analyzing speech-based suicide risk assessment. These case studies serve as the foundation for a broader critique of editorial decision-making in clinical publishing, revealing persistent structural blind spots in evaluating AI-integrated research. To prevent the pre-mature adoption of flawed models in clinical care, this perspective proposes targeted reforms: recruiting technically proficient reviewers, mandating transparent methodological reporting, and protecting space for independent post-publication evaluation. Without such changes, the integrity of the field and the safety of patients remain at risk.