Addressing 6 challenges in generative AI for digital health: A sco** review

T Templin, MW Perez, S Sylvia, J Leek… - PLOS digital …, 2024‏ - journals.plos.org
Generative artificial intelligence (AI) can exhibit biases, compromise data privacy,
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

Y Yang, M Lin, H Zhao, Y Peng, F Huang… - Journal of Biomedical …, 2024‏ - Elsevier
Objectives Artificial intelligence (AI) systems have the potential to revolutionize clinical
practices, including improving diagnostic accuracy and surgical decision-making, while also …

Sadm: Sequence-aware diffusion model for longitudinal medical image generation

JS Yoon, C Zhang, HI Suk, J Guo, X Li - International Conference on …, 2023‏ - Springer
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 …

Art or artifact: evaluating the accuracy, appeal, and educational value of AI-generated imagery in DALL· E 3 for illustrating congenital heart diseases

MH Temsah, AN Alhuzaimi, M Almansour… - Journal of Medical …, 2024‏ - Springer
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 …

Sdf4chd: Generative modeling of cardiac anatomies with congenital heart defects

F Kong, S Stocker, PS Choi, M Ma, DB Ennis… - Medical Image …, 2024‏ - Elsevier
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural
abnormalities, often requiring customized treatment plans for individual patients …

Cheart: A conditional spatio-temporal generative model for cardiac anatomy

M Qiao, S Wang, H Qiu, A De Marvao… - IEEE transactions on …, 2023‏ - ieeexplore.ieee.org
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 …

Generative AI unlocks PET insights: brain amyloid dynamics and quantification

MN Bossa, AG Nakshathri, AD Berenguer… - Frontiers in Aging …, 2024‏ - frontiersin.org
Introduction Studying the spatiotemporal patterns of amyloid accumulation in the brain over
time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography …

Adversarial counterfactual augmentation: application in Alzheimer's disease classification

T **a, P Sanchez, C Qin, SA Tsaftaris - Frontiers in radiology, 2022‏ - frontiersin.org
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 …

ONLS: Optimal Noise Level Search in Diffusion Autoencoders Without Fine-Tuning

Z Wang - The Second Tiny Papers Track at ICLR 2024, 2024‏ - openreview.net
An ideal counterfactual estimation should achieve balance of precise intervention and
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

D Wilczok - Aging (Albany NY), 2025‏ - pmc.ncbi.nlm.nih.gov
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 …