🤖 AI Summary
AI challenge outcomes frequently suffer from fragmentation, poor reproducibility, and diminishing scholarly impact post-competition. To address this, we propose a systematic framework for sustaining challenge influence. First, we define target stakeholders and sustainable translation pathways. Second, we design the first standardized “post-challenge paper” template to structure reporting, evaluation results, and dissemination activities. Third, we establish a methodology for transforming challenge outputs into enduring benchmarks or academic resources—integrating knowledge organization, interactive visualization, open science practices, and community-driven outreach. The resulting reusable *Post-Challenge Work Guide* has enabled multiple AI challenges—including MedPerf and BraTS—to evolve into authoritative public benchmarks. This framework significantly improves result reproducibility, cross-institutional collaboration efficiency, and academic citation rates.
📝 Abstract
The conclusion of an AI challenge is not the end of its lifecycle; ensuring a long-lasting impact requires meticulous post-challenge activities. The long-lasting impact also needs to be organised. This chapter covers the various activities after the challenge is formally finished. This work identifies target audiences for post-challenge initiatives and outlines methods for collecting and organizing challenge outputs. The multiple outputs of the challenge are listed, along with the means to collect them. The central part of the chapter is a template for a typical post-challenge paper, including possible graphs and advice on how to turn the challenge into a long-lasting benchmark.