Background and scope
Imaging is a crucial component of cancer clinical protocols, providing detailed morphological, structural, metabolic, and functional information. However, harnessing the full potential of the data generated through medical imaging in clinical settings remains challenging. Clinicians often struggle to combine diverse and large-scale data into a comprehensive view of patient care, disease progression, and treatment efficacy. The inability to seamlessly integrate and interpret diverse data sources result in suboptimal patient outcomes and inefficiencies in the delivery of healthcare.
The integration of traditional Artificial Intelligence (AI) with medical imaging can transform healthcare, but most existing applications are still in their infancy and must overcome a number of challenges to accelerate adoption. These include AI applications being confined to single data modalities, which restricts their overall effectiveness (Monomodal Application); inadequate and insufficient data training, leading to data scarcity and a lack of generalizability, making them less reliable across diverse patient populations, including with regard to gender-sensitivity; and the lack of AI model interpretability, as many AI systems function as "black boxes," providing little insight into their decision-making processes. This lack of transparency limits trust in the systems and their usability in clinical settings.
The goal of this Pathfinder Challenge is to create interactive
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