Introduction: Algorithmic bias in systematic reviews that use automatic screening is a major challenge in the application of AI in health sciences. This article presents preliminary findings from the project titled Identification, Reporting, and Mitigation of Algorithmic Bias in Systematic Reviews with AI-Assisted Screening: Systematic Review and Development of a Checklist for its Evaluation registered in PROSPERO with the registration number CRD420251036600 (https://www.crd.york.ac.uk/PROSPERO/view/CRD420251036600). The results presented here are preliminary and part of ongoing work. Objective: To synthesize knowledge about the taxonomies of algorithmic bias, reporting, relationships with classical biases, and use of visualizations in AI-supported systematic reviews in health sciences. Methods: A specific literature review was conducted, focusing on systematic reviews, conceptual frameworks, and reporting standards for bias in AI in healthcare, as well as studies cataloguing detec-tion and mitigation strategies, with an emphasis on taxonomies, transparency practices, and visu-al/illustrative tools. Results: A mature body of work describes stage-based taxonomies and mitigation methods for algo-rithmic bias in general clinical AI. Common improvements in reporting and transparency (e.g. CON-SORT-AI, SPIRIT-AI) are described. However, there is a notable absence of direct application to AI-automated screening of systematic reviews or empirical analyses of the interactions of biases with classical biases at the review level. Visualization techniques, such as bias heatmaps and pipe dia-grams, are available, but have not been adapted to review workflows. Conclusions: There are fundamental methodologies to identify and mitigate algorithmic bias in AI in health, but significant gaps remain in the understanding and operationalization of these frameworks within AI-assisted systematic reviews. Future research should address this translational gap to ensure transparency, fairness, and methodological rigor in the synthesis of evidence.

The authors have declared no competing interest.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251036600

This study did not receive any funding

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