Artificial Intelligence-enabled Meta-research on Intensive Care Unit Case Reporting: A Multijournal Evaluation Using the PROMPTS Framework with Dual Clinician Validation

evaluates journal-wise variation to guide future development of ICU-specific reporting standards. 3 Methods Study SampleForty-nine adult ICU case reports were obtained from four journals representing diverse publishers: Journal of Acute and Critical Care (JACC)-(n = 7) IntroductIonCase reports facilitate early dissemination of novel clinical observations, rare presentations, and unusual disease trajectories.In the ICU, however, patient management is characterized by continuous monitoring, dynamic physiologic shifts, and multimodal organ support.As such, ICU case reports require high-resolution documentation of ventilator parameters, vasoactive titration, perfusion metrics, laboratory trends, neurological assessments, renal support modality, and fluid balance trajectories.Yet existing case-reporting guidelines, including the CARE framework, 1 do not include explicit ICU-specific requirements.This results in inconsistent reporting, missing physiologic data, and insufficient detail to reproduce clinical reasoning.Artificial intelligence (AI) provides a scalable mechanism to assess documentation quality. 2 PROMPTS A-C is a structured framework designed to classify three types of deficiencies: A: Internal inconsistencies.