DECIDE-AI is a stage specific reporting guideline for the early, small scale and live clinical evaluation of decision support systems based on artificial intelligenceThe DECIDE-AI checklist presents 27 items considered as minimum reporting standards. It is the result of a consensus process involving 151 experts from 18 countries and 20 stakeholder groups DECIDE-AI aims to improve the reporting around four key aspects of early stage live AI evaluation: proof of clinical utility at small scale, safety, human factors evaluation, and preparation for larger scale summative trials

he prospect of improved clinical outcomes and more efficient health systems has fueled a rapid rise in the development and evaluation of AI systems over the last decade. Because most AI systems within healthcare are complex interventions designed as clinical decision support systems, rather than autonomous agents, the interactions among the AI systems, their users and the implementation environments are defining components of the AI interventions' overall potential effectiveness. Therefore, bringing AI systems from mathematical performance to clinical utility needs an adapted, stepwise implementation and evaluation pathway, addressing the complexity of this collaboration between two independent forms of intelligence, beyond measures of effectiveness alone 1 . Despite indications that some AI-based algorithms now match the accuracy of human experts within preclinical in silico studies 2 , there

Key Points Question Is clinician diagnostic performance associated with the use of machine learning–based clinical decision support systems? Findings In this systematic review of 37 studies, no robust evidence was found to suggest an association between the use of machine learning–based clinical algorithms to support rather than replace human decision-making and improved clinician diagnostic performance. Meaning Caution should be observed when estimating the current ability of machine learning algorithms to affect patient care, and emphasis on the evaluation of the human-computer interaction is needed.

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Objective: To define reporting standards for IDEAL format studies. Background:The IDEAL Framework and Recommendations establish an integrated pathway for evaluation of new surgical techniques and complex therapeutic technologies. However guidance on implementation has been incomplete, and incorrect use is commonly seen. We Conclusions: Participants familiar with IDEAL called for reporting guidelines for studies in all IDEAL stages except stage 3. The checklists developed have the potential to improve standards of reporting and thereby advance the quality of research on surgery and complex interventions and technologies, but require further evaluation in use.