Please see below for the next talk in the fall seminar series organized by the Institute for AI & Computational Research on AI/ML techniques and applications across various scientific domains. You can find a table of upcoming talks here: https://web.uri.edu/iacr/seminars/. Note the special time and date for this seminar.Speaker: Ritambhara Singh (Brown) Date/Time/Location: Fri, Oct. 17, noon, Memorial Union room 308. Title: The promise and pitfalls of integrative AI in healthAbstract: Artificial intelligence (AI) is increasingly being integrated into health and medicine, with the promise of revolutionizing how we diagnose disease, discover new therapies, and personalize care. Integrative AI methods—approaches that combine data from multiple sources, such as graphs, medical images, electronic health records, and genomic profiles—offer a unique opportunity to capture the complexity of human health in ways that no single data type can achieve. For example, we have shown that AI methods that merge domain-specific knowledge graphs with generalized reasoning approaches can support out-of-the-box drug-based predictions. Similarly, integrating diverse types of medical data—such as genomic information, clinical records, and imaging—can enable more accurate and personalized predictions than any single data source alone.Yet the path forward is far from straightforward. Not every integration is beneficial, and in some cases, combining information can add noise or introduce hidden biases. Our recent experience with vision–language models (VLMs), which integrate text and images, highlights these concerns regarding correctness, consistency, and trustworthiness. These challenges remind us that integrative AI is not simply about adding more data, but about ensuring that the integration is meaningful and reliable.In this talk, I will explore both the promise and pitfalls of integrative AI in health. We will examine how integration can accelerate discovery, while also exploring the questions that arise for AI methods being applied in medicine. By weighing both opportunities and limitations, this talk invites us to think critically about how to advance integrative AI responsibly in the health domain.