Related papers: Artificial intelligence-enabled precision medicine…
Multimodal (MM) learning is emerging as a promising paradigm in biomedical artificial intelligence (AI) applications, integrating complementary modality, which highlight different aspects of patient health. The scarcity of large…
Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR),…
Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges to developing the early…
The U.S. Medicaid program is experiencing critical challenges that include rapidly increasing healthcare costs, uneven care accessibility, and the challenge associated with addressing a varied set of population health needs. This paper…
Skin diseases impose a substantial burden on global healthcare systems, driven by their high prevalence (affecting up to 70% of the population), complex diagnostic processes, and a critical shortage of dermatologists in resource-limited…
Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists. Early detection is crucial for improving patient survival rates. However, performing…
Recent advances in deep learning and on-device inference could transform routine screening for skin cancers. Along with the anticipated benefits of this technology, potential dangers arise from unforeseen and inherent biases. A significant…
Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines…
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized…
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need…
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on…
Convolutional Neural Networks (CNNs) can play a key role in Medical Image Analysis under large-scale annotated datasets. However, preparing such massive dataset is demanding. In this context, Generative Adversarial Networks (GANs) can…
In healthcare, artificial intelligence (AI) has been changing the way doctors and health experts take care of people. This paper will cover how AI is making major changes in the health care system, especially with nutrition. Various machine…
Artificial intelligence (AI) in healthcare is a potentially revolutionary tool to achieve improved healthcare outcomes while reducing overall health costs. While many exploratory results hit the headlines in recent years there are only few…
Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature subtle lesions and…
How can we use generative AI to design tools that augment rather than replace human cognition? In this position paper, we review our own research on AI-assisted decision-making for lessons to learn. We observe that in both AI-assisted…
In the past several years, we have taken advantage of a number of opportunities to advance the intersection of next generation high-performance computing AI and big data technologies through partnerships in precision medicine. Today we are…
The adoption of artificial intelligence in dermatology promises democratized access to healthcare, but model reliability depends on the quality and comprehensiveness of the data fueling these models. Despite rapid growth in publicly…
Melanoma is the most lethal subtype of skin cancer, and early and accurate detection of this disease can greatly improve patients' outcomes. Although machine learning models, especially convolutional neural networks (CNNs), have shown great…
Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how generative models can enhance automotive tasks, such as static…