Seminars at the Franklin: Bartek Papiez
The Rosalind Franklin Institute is welcoming Bartek Papiez on Thursday 14 November from 10:30.
To watch the seminar online, please sign up via the link below.
RAL site pass holders are welcome to attend in person in the Franklin’s first floor Hub. To join in person, please arrive in the R113 Franklin foyer at least 5 minutes before the start of the seminar and a member of the team will let you into the building.
Title:
Beyond Beautiful Images: AI and Multimodal Data in Healthcare
Abstract:
In recent years, artificial intelligence (AI) has evolved from generating stunning artwork and immersive virtual realities to becoming a transformative force in healthcare, particularly in disease detection and monitoring. While AI’s ability to create visually appealing images often takes centre stage, its true potential lies in diagnosing, predicting, and assessing diseases – transforming how we approach healthcare.
In this talk, I will present several examples of AI-driven multimodal data analysis in healthcare, with a focus on its application in the assessment of spinal diseases and cardiopulmonary conditions. Additionally, I will demonstrate how AI, when integrated with diverse data sources, is driving new discoveries, including the creation of the first digital atlas of foetal brain development. These examples illustrate AI’s power to analyse complex datasets and uncover new insights into human health.
Biography:
At the Big Data Institute, Bartek has established an independent research group the focuses on medical imaging and machine learning. His work focusses on new, multidisciplinary research projects that integrate imaging and non-imaging modalities, driving the development of innovative image analysis and machine leaning algorithms.
Notably, his research projects encompass both the theoretical foundations of AI/ML algorithms (such as image quality, image segmentation, or image registration), and applied AI/ML for longitudinal disease monitoring (using imaging, patient records, and Natural Language Processing), identification of disease therapeutic targets (using imaging and genetic data integration), and more recently, multimodal cancer imaging and radiogenomics.