Design and Integration of AI Solutions in Oncology and Healthcare Infrastructures
Bridging the Gap Between AI Innovation and Clinical Practice
Time: Thu 2025-11-27 09.00
Location: T2 (Jacobssonsalen), Hälsovägen 11C, Huddinge
Video link: https://kth-se.zoom.us/s/69783079643
Language: English
Subject area: Medical Technology
Doctoral student: Simone Bendazzoli , Medicinsk avbildning, Karolinska Institutet, Stockholm, Sweden
Opponent: Professor Maria Alejandra Zuluaga, EURECOM, Sophia Antipolis , Alpes-Maritimes, France
Supervisor: Professor Rodrigo Moreno, Medicinsk avbildning; Docent Chunliang Wang, Medicinsk avbildning; Professor Örjan Smedby, Medicinsk avbildning; Doktor Maria Holstensson, Karolinska Institutet, Stockholm, Sweden
QC 2025-10-22
Abstract
Artificial intelligence (AI) is driving major changes across numerous fields, with healthcare emerging as one of the areas with the greatest potential for impact. In medical imaging, AI has the potential to enhance patient care through personalized treatment planning and early disease detection, while simultaneously supporting clinicians by optimizing their workload, and automating complex tasks such as radiological image analysis. Over the past decade, substantial progress has been made in medical AI research, leading to highly accurate and robust models in controlled experimental settings. However, bringing these AI tools into everyday clinical use has proven challenging. Despite many scientific breakthroughs, only few AI systems are currently being adopted in clinical settings.
This PhD thesis focuses on understanding why that gap exists, and how to bridge it. The work explores the technical, organizational, and ethical barriers that slow down AI adoption in healthcare, and proposes new ways to make AI more practical, transparent, and trustworthy in clinical environments.
A key result of this research is MAIA, a collaborative platform designed to bring together doctors, radiologists, and AI researchers. MAIA provides a shared space where experts can jointly develop and test AI tools under realistic clinical conditions. By combining research methods with everyday medical workflows, MAIA helps accelerate the transition from experimental AI models to clinical tools. The platform has been successfully deployed in both research and hospital environments, demonstrating its effectiveness in accelerating the integration of AI into medical practice.
Building on this foundation, the thesis also introduces MONet, a framework that makes it easier to adapt and reuse state-of-the-art medical image segmentation models for different healthcare applications. It enables smooth integration of AI into various clinical settings, from federated learning across different institutions to human-in-the-loop smart annotation tools, ensuring that research innovations can be efficiently transferred into real-world practice.
Finally, as a methodological contribution to the field, the thesis investigates the incorporation of anatomical and contextual prior knowledge into existing deep learning frameworks, with the goal of improving model interpretability and anatomical awareness. These methods were evaluated on different clinical tasks, such as lung lobe segmentation on chest CT, breast cancer treatment response prediction, and lymphoma segmentation on whole-body PET/CT, with the findings suggesting that the relevance of anatomical priors is task-dependent and can vary significantly across contexts.
In summary, the thesis work aims to contribute to bridging the gap between AI research and clinical implementation by developing collaborative infrastructures, adaptable frameworks, and methodological insights that support the trustworthy, transparent, and effective integration of AI technologies in medical imaging practice.