Agenda

• Welcome Remarks by Robert Jenders and Brennan Spiegel
• Networking lunch
• Expert insights including a faculty Q&A
• Closing remarks with key takeaways

Deep Dive 1A

Moral Machines? Addressing Ethical Challenges in Clinical AI

Virginia Bartlett, Assistant Director, Center for Health Care Ethics, Cedars-Sinai & Neil Wenger, Professor, Internal Medicine, UCLA Health

Examine the ethical challenges and considerations of deploying AI in clinical settings. This session will discuss the balance between innovation and ethical responsibility, focusing on maintaining patient trust and safety. Engage with experts on how to navigate the moral implications of AI applications to ensure they are used justly and beneficially in healthcare.

Deep Dive 1C

Benchmarking AI: Standards for Quality and Effectiveness in Healthcare

Laura Prichard, Senior Data Scientist, UCLA Health & 
Graciela Gonzalez-Hernandez, Vice Chair for Research and Education, Computational Biomedicine, Cedars-Sinai

Explore the evaluation processes required to assess the quality and utility of AI technologies in healthcare. This session will focus on establishing standards, conducting effective testing, and ensuring that AI tools meet clinical needs for accuracy and reliability. Learn about the essential methodologies and benchmarks for responsibly integrating AI into healthcare practices.

Deep Dive 2A

Precision Cohorts: 
AI Tools for Effective Clinical Trial Enrollment

Arash Naeim, Chief Medical Officer, Clinical Research, UCLA Health & Michael Farkouh, Associate Dean, Research and Clinical Trials, Cedars Sinai & Douglas Bell, Professor, Medicine, UCLA Health 

Discover how AI is enhancing cohort selection for clinical trials by efficiently parsing vast patient registries to identify ideal candidates. This session will explore the technologies that enable precise and rapid patient enrollment, enhancing the design and outcomes of clinical trials. Learn about AI tools that are setting new standards in trial efficiency and accuracy.

Deep Dive 2C

Harnessing AI for Knowledge Discovery in Biomedical Research

Corey Arnold, Professor, Radiological Sciences & Pathology and Laboratory Medicine, UCLA Health & Omolola Ogunyemi, Director, Center for Biomedical Informatics & Professor/Chair, Department of Preventive and Social Medicine, Charles Drew University

Examine how AI and machine learning are transforming the landscape of biomedical research by enabling extraction of novel insights from complex datasets. This session will explore the tools and methodologies that AI brings to the forefront of knowledge discovery, highlighting case studies where AI has identified patterns and predictions previously obscured in vast amounts of data.

Deep Dive 3A

Evaluating for Evidence of Sociodemographic Bias in AI for Mental Health Support

Omer Liran, Assistant Professor, Psychiatry and Behavioral Neurosciences, Cedars-Sinai & Yee Hui Yeo, Resident, IM Gastroenterology, Cedars-Sinai

Critically examine AI applications in mental health support and learn to identify and address potential sociodemographic biases. This session will explore methodologies for detecting biases that may affect treatment outcomes across different populations. Participants will learn about tools and techniques for ensuring that AI-driven mental health interventions are equitable.

Deep Dive 3C

Beyond Bias: Envisioning a Sustainable and Equitable Future for AI in Biomedicine  

Maneesh Juneja, Planetary Health Futurist, MJ Analytics Ltd  

Step into the future of healthcare, where AI is not only equitable but also environmentally responsible. This workshop explores how mitigating bias in AI may actually reduce its carbon footprint. We'll delve into cutting-edge techniques like federated learning, which allows AI models to be trained across decentralized data sources, reducing the need for energyintensive data centers. We'll also explore how synthetic data generation can create diverse and representative datasets without the environmental costs associated with traditional data collection. But it's not just about the tech – we'll dive into the ethical dilemmas and societal impacts of these advancements. Get ready to challenge your assumptions, contribute your vision, and help define a future where AI in biomedicine is both fair and sustainable