Workshops

Sunday | November 10, 2024 | 9:00 – 12:30

The integration of artificial intelligence (AI) into tele-echography holds the potential to revolutionize medical diagnostics, especially in remote and hard-to-reach areas. Tele-echography can enable remote imaging for cardiac, lung, musculoskeletal, breast, and prenatal assessments, greatly enhancing diagnostic accuracy and accessibility. However, despite technological advancements, the implementation of tele-echography encounters challenges such as diagnostic variability, data privacy concerns, integration issues, technology access disparities, and the lack of practical procedures for obtaining clinically useful ultrasound images at home.

In this workshop, we will discuss these challenges, cutting-edge research, innovative applications, and successful case studies of AI in tele-echography. From the insights provided by our expert speakers, participants will gain a comprehensive understanding of the role of AI in tele-echography in real-world applications.

Sunday | November 10, 2024 | 9:00 – 12:30

Digital Twins refer to the digital replica of a physical entity that is updated as the physical entity ages. Digital Twins, and their ability to provide AI-driven clinical decision support are central themes to BHI, with this workshop focused on novel digital health solutions based around digital twins for clinical decision support.

The idea to create a digital twin has been around for decades and has been realized in many manufacturing industries such as the auto industry. The digital twin technologies have been used to simulate and monitor the behavior of a physical object prior to manufacturing. The technology has also been used as a platform to assess the performance of a system as different components are built, replaced, and improved over time. The application of digital twins in healthcare and in particular in cardiometabolic health is relatively new. The ability to create a digital replica of a human organ or living system to perform health assessment, simulate treatments, and measure health outcome trajectories through the digital twin opens new avenues for intervention design, treatment discovery, and device development and testing.

Sunday | November 10, 2024 | 13:30 – 17:00

Computational modeling give opportunity for a patient-specific model in order to improve the quality of prediction for the disease progression into life-threatening events that need to be treated accordingly. In this workshop lectures will present computational modeling with AI as hybrid support tools for disease characterization, and the discovery of new knowledge; associations among heterogeneous data, that can improve the predictive power of the patient-model. It will support the medical expert to upgrade the accumulated knowledge into the existing model and generating an adaptive patient-specific computational tool.

Sunday | November 10, 2024 | 13:30 – 17:00

Accurate assessment of pathology severity is crucial in healthcare as it directly impacts clinical decisions, treatment planning, and patient outcomes. The growing availability of large-scale data and advancements in AI and machine learning offer unprecedented opportunities to enhance these evaluations. Discussing this topic at BHI 2024 is timely and relevant, aligning with current healthcare trends. The workshop will focus on methodological innovations, providing participants with insights into state-of-the-art techniques and best practices. This will enhance technical skills and promote interdisciplinary collaboration between clinicians and AI experts, bridging theoretical advancements and practical applications. Additionally, the workshop will address ethical considerations for AI systems, emphasizing the importance of creating responsible, patient-centric solutions

Sunday | November 10, 2024 | 13:30 – 17:00

BHI attracts innovators interested in translational cutting-edge research for healthcare applications. This topic is appropriate as the use of AI in clinical decision-making is becoming more prevalent, and these decisions can have significant moral and ethical implications, potentially exacerbating existing health disparities. Despite this, ethics is often an afterthought in model development process, a practice that is at odds with the fundamental principle of medicine: to do no harm. The main aim is to equip AI practitioners in positioning foundational ethics as an integral part of model development process, rather than a quality that models are evaluated on post-hoc. We will address technical challenges in embedding ethical principles into the very genesis of human-centric model development, representing a paradigm shift from measuring and mitigating impacts to preventing them; thus contributing to leverage Trustworthy AI. We will discuss how to practically integrate ethics in research proposal and conceptualization.