TOWARDS BETTER HEALTH & CARE - THE POTENTIAL OF AI

VU Campus Center for AI & Health

TOWARDS BETTER HEALTH AND CARE: THE POTENTIAL OF AI

Vrije Universiteit Amsterdam - Auditorium & Aurora

THANKS! We are looking forward to seeing you :)

On May 24, we will gather in the VU&#x27;s Auditorium to explore the health-, technological and implementation challenges of this exciting field. The agenda includes keynotes, short presentations/workshops, a poster session and plenty of opportunity for meeting and connecting with colleagues from around the VU Campus and further afield.Please find the full day schedule <a href="https://www.ai-health.nl/towards-better-health-care-the-potential-of-ai/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">here</a>!### KEYNOTE PRESENTATIONS ###Prof dr. Sandjai Bhulai (VU) - Artificial Intelligence in healthcare … it matters!The relevance of artificial intelligence in healthcare settings cannot be overemphasized. It has been successfully used worldwide to provide a wide range of methodologies that can help healthcare professionals significantly improve their operations. At the same time, many of the traditional problems reappear with higher complexity due to a more personalized approach due to the availability of data and IT systems. This talk will illustrate the power of artificial intelligence in healthcare systems by highlighting some key examples. Artificial intelligence can help improve healthcare services while balancing resources, quality, and costs. It matters!Prof dr. Marleen Huysman (VU) and Dr. Mohammad Rezazade Mehrizi (VU) - Managing AI in practice: A work and organizational perspectiveIn this talk, Prof. Marleen Huysman will discuss the key aspects of working with and organizing for AI technologies. Drawing on her research in a wide range of industries and organizations, she will discuss how organizations cope with implementing AI in practice. In the second part of the talk, Dr. Mohammad H. Rezazade Mehrizi focusses specifically on managing AI in the domain of radiology, discussing the trends of developments of AI tools in this domain, the challenges of implementing AI in medical practice, and lessons we can learn from leading medical centers.### &#x27;INTRODUCTION TO AI&#x27; WORKSHOP ### In a (partly) parallel session to the research programme of the day, we are offering an &#x27;Introduction to AI&#x27; workshop. The workshop will provide some tools and insights for health (care) researchers and others seeking to deepen their understanding of the technical aspects of AI. Peter Bloem: AI and Healthcare: Seeing through the hypeIn recent years, a lot has been written about the promise of AI and healthcare. As happens with new technology, reality doesn’t always live up to the promise. Without a technical understanding of the methods involved, it can be hard to separate the wheat from the chaff and to get a sense of what these technologies will actually mean for the future of healthcare. In this talk, we will look at some of the basic methods that are currently popular, such as machine learning and deep learning and explain in simple terms how they work. We will also look at some examples of where promising technology didn’t live up to the early promise, and how AI researchers sometimes misunderstand the realities of healthcare or the best ways to use promising technology in a real-world setting.Mark Hoogendoorn: Machine learning for structured data in a medical settingIn this talk, we will discuss the usage of machine learning on clinical data for various purposes. We will focus on predictive modelling of diseases whereby the temporal component that is often present in the data will be taken into account. On top, we will look at identification of the best treatment of patients using reinforcement learning. Explanations of the techniques will be given by means of two case studies, namely early diagnosis of colorectal cancer and optimization of sepsis treatment.Jakub Tomczak: AI for Medical imagingIn this talk, we are going to discuss how AI could be used for medical imaging. We will highlight typical problems in medical imaging and why current AI technologies like deep learning have enabled human-level performance. We will conclude the talk with two specific examples of applying AI to medical imaging problems.This workshop is now fully booked. As the room has limited capacity, those who have signed up will get priority.### SPEAKERS ###In addition to the keynotes and workshop, we are delighted to announce our other speakers for the day:Dr. Emma Beauxis-Aussalet (VU): Assessing Classification Errors for Fair &amp; Transparent AIProf. dr. Henk Marquering (AmsterdamUMC): Translating AI into Radiological practiceDr. Jenia Kim (AmsterdamUMC) and Dr. Marike van der Leeden (VU): Automated recognition of function, activity and participation of COVID-19 patients with natural language processingSelim Sametoglu (VU): The Value of Social Media Language for the Assessment of Wellbeing: A Systematic Review and Meta-AnalysisBob van de Loo (AmsterdamUMC) and Noman Dormosch (AmsterdamUMC): Applying AI to address the complex health care problem of falls in older personsProf. Hein van Hout (AmsterdamUMC): ICARE4OLD: Predicting health trajectories and treatment impacts in elderly persons with complex chronic conditionsLukas de Clercq (AmsterdamUMC): Machine learning on routine care data for timely detection of heart failure### POSTER SESSION ###Floris den Hengst: Improving data-driven clinical decision making with medical guidelinesKhadicha Amarti and Claire van Genugten: Personalized messages as tool to increase adherence to an online self-help intervention for depressionOliver Gurney-Champion: Physics-informed deep learning for quantitative MRI modellingBomi Kim: Developing Algorithms in the Dark: Coping with an Autonomous and Inscrutable AlgorithmLea Lösch: Enacting Citizens&#x27; and Professionals’ Concerns on COVID-19 Vaccination with AIStefan Smorenburg: Automatic analysis of medical imaging obtained during surgery (abdominal aneurysms) in the hybrid operating roomTariq Dam: Personalized proning: a machine learning approach to predict responders to prone positioning in intubated patients with COVID-19 ARDSFatemeh Kazemzadeh: Automatic extraction of clinicopathological information from non-small lung cancer autopsy reports### DEMOS ###There will be various demos during the poster session, including:<ul><li>Fitsurance - Data-driven lifestyle coaching</li><li>WSK Medical - AI-based throat cancer detection</li><li>Skinive - Skincare AI Assistant</li><li>Autoscriber - AI to automate the clinical note taking process</li></ul>

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TOWARDS BETTER HEALTH & CARE - THE POTENTIAL OF AI

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