AIM-AHEAD AI Optimization Subcore
About
The AIM-AHEAD AI Optimization Subcore page is a central resource for discussing and exploring AI optimization issues within the AIM-AHEAD Program. Here, you can access expert insights, engage in meaningful conversations, and find valuable resources to guide your AI decision-making.
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Participate in live presentations on current AI topics and bring your questions or concerns for group discussion and expert feedback. - Engage in the AI Optimization Discussion Group
Connect with peers and AI optimization experts to share ideas and engage in ongoing conversations on AI optimization-related matters. - Get Personalized AI Optimization Consultation
Receive tailored advice and support on questions specific to your project or activity. - Explore the Knowledgebase
Delve into a wide range of AI optimization-related topics to deepen your understanding and inform your work. - Access AIM-AHEAD-Specific AI Optimization Resources
Discover materials designed specifically for the AIM-AHEAD community, addressing key challenges in our field.
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AI Optimization Subcore Discussion Forums
This forum will examine the unexpected consequences of multi-source data scaling in healthcare. The discussion will showcase innovative applications of LLMs in maternal health and contraceptive care, demonstrate how LLMs can generate rationales for contraceptive medication switches using clinical notes, and emphasize vigilance and other considerations as we advance towards more data-driven and AI-assisted healthcare.
About the Speaker
Dr. Irene Chen is an Assistant Professor in UC Berkely and UCSF's Computational Precision Health, Electrical Engineering and Computer Science, Berkeley AI Research. Her work develops machine learning methods for healthcare that are robust and impactful, Irene received her PhD in Electrical Engineering and Computer Science from MIT, her AB/SM in Applied Math from Harvard.
Join on Zoom: AI Optimization Subcore Discussion Forum Link
This talk will cover research published in NEJM and JAMA on the clinical, occupational, and financial implications of including or excluding selected demographic features in equations used across various medical fields. It will explore the limitations of binary decision thresholds, socio-demographic model inputs, and population-derived reference ranges, and then discuss alternative approaches that prioritize precise causal measures and patient-centered outcomes.
About the Speaker
Dr. James Diao is a physician-scientist based at Brigham and Women’s Hospital and the Harvard Medical School Department of Biomedical Informatics. His research uses computational and statistical tools to develop and evaluate clinical algorithms with the goal of improving health for various populations. Previously, he developed AI models for pathology image analysis at PathAI and investigated wearable-derived measures of cardiovascular fitness at Apple. Dr. Diao earned his MD from the Harvard-MIT Program in Health Sciences and Technology (HST) as a PD Soros Fellow, MPhil from the University of Cambridge as a Churchill Scholar, and degrees in biochemistry and statistics from Yale College.
Join on Zoom: AI Optimization Subcore Discussion Forum Link
As AI applications continue to transform clinical research and care practices, ensuring their trustworthiness is critical. A key component of trust is reproducibility. Dr. Fu will discuss the various conceptual dimensions of reproducibility in the context of EHR-based AI applications. He will define the role of contextual metadata in the process of AI model development, evaluation, and dissemination, and illustrate his work on standardized frameworks, RITE-FAIR principles, and tools that promote AI reproducibility.
About the Speaker
Dr. Sunyang Fu, PhD MHI, is an Assistant Professor and Associate Director of Team Science at the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI), University of Texas Health Science Center at Houston. The overarching goal of my research is to accelerate, improve and govern the secondary use of EHR data for clinical and translational research toward high throughput, reproducible, and trustworthy discoveries.
Zoom link: https://us06web.zoom.us/j/84694432646?pwd=LfXyYerpK2ErARq5ha6b6DMfBb3COb.1
Community Health Workers (CHWs), or Promotores de Salud in Latino communities, help address healthcare challenges by providing education and services to targeted populations. To strengthen their research skills, the "Building Research Integrity and Capacity" (BRIC) initiative offers eight training modules for self-paced learning or professional development. A complementary course was also created to help faculty better engage local communities in research. Dr. Nebeker will discuss the co-design and evaluation of these educational resources.
About the Speaker
Dr. Camille Nebeker, EdD, MS, is Co-Founder and Director at UC San Diego Research Center for Optimal Digital Ethics - Health, Professor at Herbert Wertheim School of Public Health and Human Longevity Science, and Director of UC San Diego Research Ethics Program, University of California, San Diego.
Zoom link: https://us06web.zoom.us/j/84694432646?pwd=LfXyYerpK2ErARq5ha6b6DMfBb3COb.1