iDPP@CLEF 2023 (Intelligent Disease Progression Prediction at CLEF) is a challenge organized by the BRAINTEASER Horizon 2020 project and co-located with CLEF 2023 (Conference and Labs of the Evaluation Forum). BRAINTEASER is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data. The goal of iDPP@CLEF is to design and develop an evaluation infrastructure for AI algorithms able to: Better describe disease mechanisms. Stratify patients according to their phenotype assessed all over the disease evolution. Predict disease progression in a probabilistic, time-dependent fashion. iDPP@CLEF 2023 built upon iDPP@CLEF 2022, expanding the tasks of the previous edition and providing novel tasks. iDPP@2022 repositories can be found at https://zenodo.org/records/7477919. iDPP@CLEF 2023 offered the following tasks: Task 1: Predicting Risk of Disease Worsening (MS): It focuses on MS and requires ranking subjects based on the risk of worsening, setting the problem as a survival analysis task. Worsening is defined on the basis of the Expanded Disability Status Scale (EDSS). More specifically the risk of worsening predicted by the algorithm should reflect how early a patient experiences the "worsening" event.< Task 2: Predicting Cumulative Probability of Worsening (MS): It refines Task 1 by asking participants to explicitly assign the cumulative probability of worsening at different time windows, i.e., between years 0 and 2, 0 and 4, 0 and 6, 0 and 8, 0 and 10. Task 3: Position Papers on the Impact of Exposition to Pollutants (ALS): It requires to propose approaches to assess if exposure to different pollutants is a useful variable to predict time to PEG, NIV, and death in ALS patients. This task is based on the same design as Task 1 in iDPP@CLEF 2022 and employs the same data as well, with the addition of environmental data. This dataset contains the repositories of the participants to iDPP@CLEF 2023. These repositories contain the output, i.e. the predictions, produced by the participating systems as well as the performance scores for those systems. For additional information about iDPP@CLEF 2023, please see: Guglielmo Faggioli, Alessandro Guazzo, Stefano Marchesin, Laura Menotti, Isotta Trescato, Helena Aidos, Roberto Bergamaschi, Giovanni Birolo, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Enrico Longato, Sara C. Madeira, Umberto Manera, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo, Nicola Ferro: Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023. CLEF 2023: 343-369. Lecture Notes in Computer Science (LNCS) 14163, Springer, Heidelberg, Germany. Guglielmo Faggioli, Alessandro Guazzo, Stefano Marchesin, Laura Menotti, Isotta Trescato, Helena Aidos, Roberto Bergamaschi, Giovanni Birolo, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Enrico Longato, Sara C. Madeira, Umberto Manera, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo, Nicola Ferro: Overview of iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge. CLEF (Working Notes) 2023: 1123-1164. http://ceur-ws.org/Vol-3497/.