Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 25, 2024
Date Accepted: Jan 22, 2025
Reporting Quality of Artificial Intelligence Intervention in Randomized Controlled Trials in Primary Care: A Systematic Review and Meta-Epidemiological Study
ABSTRACT
Background:
The surge in artificial intelligence (AI) interventions in primary care lacks a comprehensive study on trial reporting quality.
Objective:
To systematically evaluate the reporting quality both of the published RCTs and the protocols for RCTs that investigated AI interventions in primary care.
Methods:
PubMed, EMBASE, and Cochrane Library databases were searched for RCTs and protocols on AI interventions in primary care until May 2023. Reporting quality was assessed using CONSORT-AI and SPIRIT-AI extension checklists.
Results:
A total of 6,052 unique records were identified. Nine published articles and 17 RCT protocols totally for 23 trials were included. The overall proportion of adequately reported items was 65.1% (95% CI: 56.4-72.9%) and 69.8% (95% CI: 63.9-75.1%) for RCTs and protocols respectively. The percentage of RCTs and protocols that reported a specific item, respectively, ranged from 11.1% to 100% and from 5.9% to 100%. Both RCTs and protocols reporting 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/or its code can be accessed.
Conclusions:
The reporting quality could be improved in both RCTs and protocols. This study helps to promote the transparent and complete reporting of trials with AI interventions in primary care, facilitating to AI application in real-world settings.
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