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Found 6 papers, 1 papers with code
Foundation Models in Radiology: What, How, When, Why and Why Not
Given the potentially transformative impact that foundation models can have on the field of radiology, this review aims to establish a standardized terminology concerning foundation models, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies.
Transforming the Hybrid Cloud for Emerging AI Workloads
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability.
Towards Pareto Optimal Throughput in Small Language Model Serving
Large language models (LLMs) have revolutionized the state-of-the-art of many different natural language processing tasks.
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality.
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
