Workshop: Health AI Systems Thinking for Equity

Registration and participation fee
Due to the high demand for this workshop, we have introduced an application process to ensure a balanced and diverse representation of professional backgrounds and interests.
Participation fee: 50 DKK (no-show: 500 DKK)

To apply for a ticket, please complete the participation application form via this Google Forms Link.
You will be notified of your application status within one week of submission. If your application is approved, you will receive a link to purchase your ticket at 50 DKK.
Please note, a no-show fee of 500 DKK will apply to ensure commitment. We encourage you to apply as soon as possible, as seats will be allocated on a rolling basis, and we anticipate reaching capacity quickly. Eligible applicants whose applications arrive after all seats have been filled will be placed on a waiting list and contacted if a spot becomes available.

Workshop Programme:
Artificial intelligence (AI) has the potential to transform healthcare worldwide. bearing promises of increased accuracy, efficiency, and cost-effectiveness, in areas as diverse as drug discovery, clinical diagnosis, and disease management.

Furthermore, AI has been promoted as a tool that could expand the reach of quality healthcare to traditionally underserved patients and regions. But even with appropriate representation of marginalized communities with high-quality data, the social patterning of the data generation process can still produce AI that is bound to preserve and even scale existing disparities in care with resulting inequities in patient outcomes. 

Creating algorithms from the digital exhaust of flawed human systems by AI developers who are not cognizant of the backstory of the data, risks cementing inequities as permanent fixtures in healthcare delivery systems. This course will introduce students to a portfolio of methodologies that learn patterns from the data. More importantly, it will explore data issues that if not addressed will have profound consequences on downstream prediction, classification, and optimization tasks. 

Learning Objectives / Key Takeaways
Upon successful completion of this course, you should be able to:  

• Work with data scientists, social scientists, and clinicians across the life cycle of health AI and apply systems thinking to the application of AI to healthcare  
• Learn good code documentation for reproducibility of AI development 
• Develop a critical understanding of how the dataset came about from collection to aggregation to standardization
• Perform exploratory data analysis with a special emphasis on data bias
• Understand the basic principles of different machine learning methodologies
• Interpret and communicate analysis results
• Think about potential downstream harm from algorithm implementation

Who should participate
Students, scientists, and analysts engaged in development, deployment or assessment and analysis of AI in healthcare and open to cross-disciplinary collaboration. 

Speakers

• Leo Anthony Celi Associate Professor at Harvard Medical School, and Clinical Research Director of the Laboratory of Computational Physiology at the MIT
• Martin Sillesen Clinical Research Lecturer in Surgery, Rigshospitalet. Brings clinical insights into health technology research, with a special interest in the applications of AI in surgical practices. 
• Anna Schneider-Kamp. Qualitative Health Researcher, Associate Professor, Department of Business and Management, University of Southern Denmark
  Specializes in qualitative health research, with a focus on the intersection of health, business, and management practices. 
• Matilda Dorotic. Associate Professor, Department of Marketing, BI Norwegian Business School, Norway. Expert in incentive structures and marketing strategies in healthcare, studying how market mechanisms influence patient and provider behavior. 
• Ericka Johnson Professor, echnology and Social Change, Linköping University
  Focuses on the social impacts of technology, including ethical frameworks and social challenges associated with health AI. 
• Mads Bundgaard Nørløv MSc BME student, Johns Hopkins Center for Bioengineering Innovation and Design & Founder/Chair, Copenhagen MedTech Innovator in bioengineering with expertise in medtech entrepreneurship, fostering cross-disciplinary collaborations in health technology. 
• João Matos PhD Student, University of Oxford Researching applications of AI in healthcare with a focus on ethical considerations in patient data management. 
• David Restrepo PhD Student, Applied Mathematics, CentraleSupélec, University Paris-Saclay. Specialist in mathematical modeling for healthcare, exploring new applications of AI in medical diagnostics. 
• Chris Sauer MD, MPH, PhD, Physician, Universitätsmedizin Essen, and MIT Researcher Medical professional and researcher focused on integrating AI with medical practice to improve patient outcomes. 
• Nikolaj Munch Andersen. Senior Tech Advisor, Danish Ministry of Foreign Affairs (Udenrigsministeriet) Advisor on technology policy with a focus on AI regulations and international tech governance. 

Agenda 

• 08:30 Registration and Breakfast 
• 09:00 Welcome and Opening Remarks
  Speakers: Leo Anthony Celi, Martin Sillesen and Henning Boje Andersen 
• 09:30 Panel Discussion: "Beyond the Bottom Line: Which Capitals Drive Health AI?"
  Panelists: Anna Schneider-Kamp and Martin Sillesen
  Exploring the allocation of economic and sociocultural resources in health AI and how it impacts inclusivity and equity in various healthcare settings. 
• 10:15 Coffee Break
• 10:25 Panel Discussion: "Reimagining Incentive Structures to Safe-Proof Health AI"
  Panelists: Matilda Dorotic and Mads Nielsen
  A critical discussion on how incentives can be structured to prioritize patient safety and align AI advancements with healthcare goals. 
• 11:15 Panel Discussion: "Critical Thinking as a Requisite for AI Education"
  Panelists: Ericka Johnson and Niels Hansen
  Addressing the need for robust critical thinking in AI education and its role in developing ethical and responsible AI professionals. 
• 12:00 Lunch Break  
• 13:00 Workshops in parallel - Session 1
  · Introduction to Machine Learning 
  · Bias-athon · Language Model Prompt-athon 
  · Policy Workshop 
• 14:30 Coffee/cake / refreshments 
• 15:00 Workshops in parallel – Session 2 (repeat)  
• 16:30 Summing up, learnings and perspectives
  moderation by Leo Anthony Celi and Martin Sillesen
• 17:00 End of workshop
• 18:00 Dinner at IDA Conference Restaurant (free) - remember to indicate if you wish to participate.

Program Committee

• Leo Anthony Celi. Assoc. Professor Harvard Medical School; Clinical Research Director at Computational Physiology Lab / MIT
• Martin Sillesen. Assoc. Professor, Clinical Research Lecturer in Surgery, Rigshospitalet.  
• Henning Boje Andersen Professor Emeritus, Technical University of Denmark. Department of Technology, Management, and Economics / IDA Risk / DSKS Forskning. 
• Jonathan Patscheider. Vice President, Trust Stamp 
• Lasse Hyldig Hansen. Behavioural Adviser, Danish Competition and Consumer Authority; Research Assistant, Aarhus University 

Organizers

IDA Risk - IDA Engineering Society; MIT/Massachusets Institute of Technology; DSKS - Dansk Selskab for Kvalitet i Sundhedssektoren; Rigshospitalet/ Københavns Universitet; DTU Health Tech;  Copenhagen Medtech.   

Sponsor
The workshop is sponsored by DDSA – Danish Data Science Academy

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