Review 1: "Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results from the CODA TB DREAM Challenge"

This manuscript presents the results of the CODA TB DREAM Challenge, an open data challenge aimed at developing cough-based artificial intelligence (AI) algorithms for tuberculosis (TB) screening.The study addresses an important need to accelerate the development of AI tools for global health applications, particularly for TB diagnosis in low-and middle-income countries.Personally, I commend the authors for their innovative approach to bringing this idea of open innovation to the field of artificial intelligence and machine learning in public health research.Things that I liked about this research: Things that I wish were better:Reliable.The main study claims are generally justified by its methods and data.The results and conclusions are likely to be similar to the hypothetical ideal study.There are some minor caveats or limitations, but they would/do not change the major claims of the study.The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.