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Addressing 6 challenges in generative AI for digital health: A sco** review
Generative artificial intelligence (AI) can exhibit biases, compromise data privacy,
misinterpret prompts that are adversarial attacks, and produce hallucinations. Despite the …
misinterpret prompts that are adversarial attacks, and produce hallucinations. Despite the …
[HTML][HTML] A survey of recent methods for addressing AI fairness and bias in biomedicine
Objectives Artificial intelligence (AI) systems have the potential to revolutionize clinical
practices, including improving diagnostic accuracy and surgical decision-making, while also …
practices, including improving diagnostic accuracy and surgical decision-making, while also …
Sadm: Sequence-aware diffusion model for longitudinal medical image generation
Human organs constantly undergo anatomical changes due to a complex mix of short-term
(eg, heartbeat) and long-term (eg, aging) factors. Evidently, prior knowledge of these factors …
(eg, heartbeat) and long-term (eg, aging) factors. Evidently, prior knowledge of these factors …
Art or artifact: evaluating the accuracy, appeal, and educational value of AI-generated imagery in DALL· E 3 for illustrating congenital heart diseases
Abstract Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to
impact medical and patient education. This research explores the use of AI-generated …
impact medical and patient education. This research explores the use of AI-generated …
Sdf4chd: Generative modeling of cardiac anatomies with congenital heart defects
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural
abnormalities, often requiring customized treatment plans for individual patients …
abnormalities, often requiring customized treatment plans for individual patients …
Cheart: A conditional spatio-temporal generative model for cardiac anatomy
Two key questions in cardiac image analysis are to assess the anatomy and motion of the
heart from images; and to understand how they are associated with non-imaging clinical …
heart from images; and to understand how they are associated with non-imaging clinical …
Generative AI unlocks PET insights: brain amyloid dynamics and quantification
Introduction Studying the spatiotemporal patterns of amyloid accumulation in the brain over
time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography …
time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography …
Adversarial counterfactual augmentation: application in Alzheimer's disease classification
Due to the limited availability of medical data, deep learning approaches for medical image
analysis tend to generalise poorly to unseen data. Augmenting data during training with …
analysis tend to generalise poorly to unseen data. Augmenting data during training with …
ONLS: Optimal Noise Level Search in Diffusion Autoencoders Without Fine-Tuning
An ideal counterfactual estimation should achieve balance of precise intervention and
identity preservation. Recently, Classifier-Guided Diffusion Model is proven effective to …
identity preservation. Recently, Classifier-Guided Diffusion Model is proven effective to …
[HTML][HTML] Deep learning and generative artificial intelligence in aging research and healthy longevity medicine
With the global population aging at an unprecedented rate, there is a need to extend healthy
productive life span. This review examines how Deep Learning (DL) and Generative …
productive life span. This review examines how Deep Learning (DL) and Generative …