Despite the low adoption rates of artificial intelligence (AI) in respiratory medicine, its potential to improve patient outcomes is substantial. To facilitate the integration of AI systems into the clinical setting, it is essential to prioritise the development of explainable AI (XAI) solutions that improve the understanding of the AI predictions. These XAI solutions empower clinicians to collaborate effectively with AI systems, thereby enhancing the overall outcomes for patients in respiratory medicine. Unfortunately, the lack of user-centric studies in this domain has made it challenging to identify the specific aspects of explainability that are most effective in improving the adoption of AI in the real-world environment. To address this gap, we conducted a mixed-methods study of clinicians in respiratory medicine to identify the most relevant and crucial aspects of XAI solutions. Our study focused on understanding how XAI can be effectively translated into clinical practice by leveraging the expertise of doctors in the field. Because of the lack of knowledge about XAI concepts among pulmonologists a different approach is taken to regular user-centric XAI research and no direct examples of state-of-the-art XAI solutions are used. Rather the expertise of doctors is used to make them implicitly identify their needs and intents. Our findings reveal that the successful adoption of XAI solutions in respiratory medicine requires tailored solutions that address communication barriers, promote patient-centric care, and overcome AI adoption challenges. The study highlights the significance of task-specific visualisations, comprehensive explanations, preferred granularity, and the ability to mimic human judgement in successful XAI solutions. Trust and collaboration between clinicians and AI systems are essential for effective adoption, wherein AI is perceived as a colleague rather than a replacement. This ensures that clinicians can easily understand and work with the model predictions, ultimately leading to improved patient outcomes. By aligning XAI design with the needs and intents of pulmonologists, we established the importance of Co-designing solutions with domain experts and embedding XAI within clinical workflows emerged as key strategies. Our research underscores the imperative of transparency, extended validation, and continuous alignment of AI technologies with medical values. By following these principles, XAI solutions can be developed to enhance the diagnosis and treatment of respiratory illnesses, ultimately improving patient outcomes in respiratory medicine.

Data collection by means of crowdsourcing can be costly or produce inaccurate results. Methods have been proposed for solving these problems. However, it remains unclear what methods work best in scenarios with multiple similar objects of interest present in the same image, which is important for training computer vision with applications such as automatic quality control in factories. We researched which parameters are important to optimize, which methods are worth considering and what those selected methods score with regard to the parameters cost and quality. This was done through a literature review and substantiated by an experimental crowdsourcing campaign that focused on the annotation of Legos in images. It was found that the parameters to optimize were cost, optimized by reducing the time workers spent on tasks, and quality, optimized by improving the mean intersection over union value of the annotations. We concluded that majority vote, rejecting workers, majority vote adjusted to be resistant to outliers, rejecting workers with the same adjustments and decomposing tasks were the most promising methods. From our experiment we concluded that a clear trade-off exists between cost and quality. The adjusted rejecting workers method, that uses worker credibility, showed to have the highest mean quality. While the method that decomposed the components of the task and distributed them was the cheapest method to use overall and also best when looking at mean quality over cost, it was worse quality wise. These results were similar to the expected performance of the methods. From this we concluded that the best method for crowdsourcing is dependent on the error tolerance of the computer vision model that will be used and the budget available.