78 citations · 113 across the 9 of their papers we have counts for
9 papers
Toward Cultural Alignment: Human-Centered Evaluation of Multimodal AI Stories Across Five African Communities
Millicent Ochieng, Felermino D. M. A. Ali, Elizabeth A. Ankrah +8
In this paper, we examine how well AI-generated multimodal stories align with the lived practices, relationships, language, values, and visual expectations of the communities they…
Harm is not Universal: Community-Specific Toxicity Detection is Urgently Needed
Xinnuo Xu, Anja Thieme, Daniela Massiceti +6
State-of-the-art toxicity detectors for text-to-image generation adopt a one-size-fits-all approach: a single universal model applying fixed safety guidelines to all users. Our emp…
Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Nari Johnson, Deepthi Sudharsan, Hamna +7
Measurement is essential to improving AI performance and mitigating harms for marginalized groups. As generative AI systems are rapidly deployed across geographies and contexts, AI…
Reasoning Beyond Labels: Measuring LLM Sentiment in Low-Resource, Culturally Nuanced Contexts
Millicent Ochieng, Anja Thieme, Ignatius Ezeani +5
Sentiment analysis in low-resource, culturally nuanced contexts challenges conventional NLP approaches that assume fixed labels and universal affective expressions. We present a di…
MAIRA-2: Grounded Radiology Report Generation
Shruthi Bannur, Kenza Bouzid, Daniel C. Castro +18
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solut…
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Anja Thieme, Abhijith Rajamohan, Benjamin Cooper +22
Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious h…
Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Maria T. Wetscherek +18
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare a…
MAIRA-1: A specialised large multimodal model for radiology report generation
Stephanie L. Hyland, Shruthi Bannur, Kenza Bouzid +12
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s)…
Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu, Stephanie Hyland, Shruthi Bannur +16
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domai…