TY  - JOUR
AU  - Zhong, Jinjia
AU  - Zhu, Ting
AU  - Huang, Yafang
PY  - 2025
DA  - 2025/2/25
TI  - Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study
JO  - J Med Internet Res
SP  - e56774
VL  - 27
KW  - artificial intelligence
KW  - randomized controlled trial
KW  - reporting quality
KW  - primary care
KW  - meta-epidemiological study
AB  - Background: The surge in artificial intelligence (AI) interventions in primary care trials lacks a study on reporting quality. Objective: This study aimed to systematically evaluate the reporting quality of both published randomized controlled trials (RCTs) and protocols for RCTs that investigated AI interventions in primary care. Methods: PubMed, Embase, Cochrane Library, MEDLINE, Web of Science, and CINAHL databases were searched for RCTs and protocols on AI interventions in primary care until November 2024. Eligible studies were published RCTs or full protocols for RCTs exploring AI interventions in primary care. The reporting quality was assessed using CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) and SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) checklists, focusing on AI intervention–related items. Results: A total of 11,711 records were identified. In total, 19 published RCTs and 21 RCT protocols for 35 trials were included. The overall proportion of adequately reported items was 65% (172/266; 95% CI 59%-70%) and 68% (214/315; 95% CI 62%-73%) for RCTs and protocols, respectively. The percentage of RCTs and protocols that reported a specific item ranged from 11% (2/19) to 100% (19/19) and from 10% (2/21) to 100% (21/21), respectively. The reporting of both RCTs and protocols exhibited similar characteristics and trends. They both lack transparency and completeness, which can be summarized in three aspects: without providing adequate information regarding the input data, without mentioning the methods for identifying and analyzing performance errors, and without stating whether and how the AI intervention and its code can be accessed. Conclusions: The reporting quality could be improved in both RCTs and protocols. This study helps promote the transparent and complete reporting of trials with AI interventions in primary care. 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e56774
UR  - https://doi.org/10.2196/56774
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39998876
DO  - 10.2196/56774
ID  - info:doi/10.2196/56774
ER  -