Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Lerf: Language embedded radiance fields
Humans describe the physical world using natural language to refer to specific 3D locations
based on a vast range of properties: visual appearance, semantics, abstract associations, or …
based on a vast range of properties: visual appearance, semantics, abstract associations, or …
A foundation model for generalizable disease detection from retinal images
Medical artificial intelligence (AI) offers great potential for recognizing signs of health
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
Detrs with collaborative hybrid assignments training
In this paper, we provide the observation that too few queries assigned as positive samples
in DETR with one-to-one set matching leads to sparse supervision on the encoder's output …
in DETR with one-to-one set matching leads to sparse supervision on the encoder's output …
Explainable AI (XAI): Core ideas, techniques, and solutions
As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …
transparent and interpretable models. In addition, the ability to explain the model generally …
Driving with llms: Fusing object-level vector modality for explainable autonomous driving
L Chen, O Sinavski, J Hünermann… - … on Robotics and …, 2024 - ieeexplore.ieee.org
Large Language Models (LLMs) have shown promise in the autonomous driving sector,
particularly in generalization and interpretability. We introduce a unique objectlevel …
particularly in generalization and interpretability. We introduce a unique objectlevel …
Human-in-the-loop machine learning: a state of the art
E Mosqueira-Rey, E Hernández-Pereira… - Artificial Intelligence …, 2023 - Springer
Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …
algorithms generically called human-in-the-loop machine learning. Depending on who is in …
Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …