Frontiers of AI in Medical Imaging and Radiation Therapy
Description
Artificial Intelligence (AI) has undergone transformative advancements, revolutionizing medical imaging and radiation therapy with unprecedented precision and efficiency. Breakthroughs in agentic AI, foundation models, and interpretable AI have significantly enhanced AI’s capabilities, transparency, and clinical adoption, positioning it as a cornerstone of precision medicine in radiology and radiation oncology. This symposium will explore the cutting-edge frontiers of AI in these fields, focusing on three pivotal areas: (1). Agentic AI: Evolving beyond reactive tools, agentic AI systems leverage multi-step reasoning and autonomous decision-making to optimize complex workflows. In medical imaging and radiotherapy, these models streamline tasks such as diagnosis, image segmentation and treatment planning, to substantially improve patient care efficiency and enhance treatment outcomes. (2). Foundation Models: Large foundation models have made remarkable progress in recent years and have significantly enhanced the capabilities of AI, making it increasingly competitive with human intelligence. Recent studies have demonstrated the promise of using foundation models to enhance image processing, reconstruction and registration, opening new avenues to address challenges in image guided radiation therapy. (3). Interpretable AI: Interpretability is paramount for clinical trust and regulatory compliance. Recent innovations, such as attention-based models and explainability tools enhance AI transparency without compromising performance, fostering adoption in high-stakes settings like cancer diagnostics and therapy planning. In summary, this symposium will provide a comprehensive review of these advancements, addressing opportunities, technical and ethical challenges, and their transformative potential for clinical practice in both imaging and therapy. Attendees will gain insights into AI’s expanding role in revolutionizing radiology and radiation oncology, driving greater efficiency, and improving outcomes for cancer patients.