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Reporting Guideline for the Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of Decision support systems driven by Artificial Intelligence (DECIDE-AI). Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings
Medigy Insights
Despite promising performance in preclinical evaluation, few AI-based clinical decision support systems have demonstrated real benefits to patient care. Early-stage clinical evaluation is crucial for assessing clinical performance, safety, human factors, and paving the way for large-scale trials. However, the reporting of these early studies is inadequate. To address this, we present DECIDE-AI, a multi-stakeholder, consensus-based reporting guideline. Through extensive consultation, DECIDE-AI provides a checklist of key reporting items for early-stage clinical investigations of AI-driven decision support systems in healthcare. By enhancing the transparency, replicability, and critical appraisal of these studies, DECIDE-AI aims to facilitate the integration and optimization of AI-driven systems in clinical practice.
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