This paper presents a comprehensive overview of the second edition of the ImageCLEFfusion task, held in 2023. The primary goal of this endeavor is to facilitate the advancement of late fusion or ensembling methodologies, which possess the capability to leverage prediction outcomes derived from pre-computed inducers to generate superior and enhanced prediction outputs. The present iteration of this task encompasses three distinct challenges: the continuation of the previous year’s regression challenge utilizing media interestingness data, where performance is measured via the mAP at 10 metric; the continuation of the retrieval challenge involving image search result diversification data, where performance is measured via the F1-score and Cluster Recall at 20; and the addition of a new multi-label classification task focused on concepts detection in medical data, where performance is measured via the F1-score. Participants were provided with a predetermined set of pre-computed inducers and were strictly prohibited from incorporating external inducers during the competition. This ensured a fair and standardized playing field for all participants. A total of 23 runs were received and the analysis of the proposed methods shows diversity among them ranging from machine learning approaches that join the inducer predictions to ensemble schemes that learn the results of other methods.