AI Workshop Series
The AI workshop series aims to educate attendees on leveraging AI basic principles and tools, its ethics to improve productivity, facilitate work processes, and address ethical considerations.
We want to bridge the gap between novice users and advanced AI applications while fostering collaboration and practical hands-on learning.
The series is open to anyone interested in these topics. Each session is one hour and has a hybrid format. Sessions will be held in the Regenstrief Social Hub, with a virtual option for online attendance. In-person capacity is 100. There is no limit on virtual participation. Each session will be recorded and made available to all.
Parking will be available for in-person attendees, but we cannot provide validation.
There are no prerequisites to participate at this time.
Upcoming Sessions
Presenter: Peter Schwartz, MD, PhD
This presentation and discussion will address key ethical questions facing researchers building and testing AI in the generative era and help attendees find ways forward for their own work.
Presenter: Rachel Patzer, PhD, MPH
Synopsis available soon
Presenter: Lynsey Delp and Melissa Pangelinan, PhD
Synopsis available soon
Past Sessions (With Video)
Presenter: Kosali Simon, PhD
Watch the recording on YouTube
This session will explore how researchers across disciplines can harness the power of large language models (LLMs)–like ChatGPT and other AI tools–to enhance research productivity and innovation. We’ll walk through practical use cases for AI in literature synthesis, protocol refinement, data management, coding assistance, and pre- peer review diagnostics. Special attention will be given to how LLMs can support those with minimal research data programming experience, using small dataset examples and natural language prompts. The talk will also address responsible use, evolving norms for acknowledgment, and the emerging “safe harbors” for ethical integration of AI in research. Whether you’re working with complex code or developing clinical protocols, this presentation will offer accessible tools and guidance for integrating AI into your workflow.
Presenter: Shaun Grannis, MD
Watch the recording on YouTube
This lecture will introduce participants to the rapidly evolving world of artificial intelligence (AI) and its transformative potential in health research. The session begins by demystifying key AI concepts, including machine learning, deep learning, large language models (LLMs), and generative approaches, before diving into real-world applications in clinical research.
Attendees will explore practical use cases such as automated chart summarization, natural language querying of clinical databases, and diagnostic image analysis. The lecture also outlines the core infrastructure and deployment strategies—whether in the cloud, on-premises, or hybrid environments—needed to support successful AI integration.
Through illustrative case studies in COVID-19 and chronic disease management, the session highlights how AI has already improved clinical workflows, triage, and population health analytics. The talk includes a discussion on effective prompting strategies, fine-tuning for domain specificity, and the ethical and competency considerations vital for responsible AI use in healthcare research.
Contact
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Medina Sydykanova