Generative artificial intelligence in medical imaging: Current landscape, challenges, and future directions DOI Creative Commons
Wenle He,

Xuewei Wu,

Zhe Jin

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

Abstract The clinical significance, digital attributes, and underlying high‐dimensional information in medical images make them a key area for the artificial intelligence (AI) revolution health care. Generative AIs (GAIs) provide unprecedented abilities synthesizing diverse accurate simulated AI model training as well personalized disease management. However, several hurdles must be overcome prior to implementation, such biases introduced during synthesized risk of research falsification. This review outlines current landscape image synthesis through GAIs, with specific focus on variety synthesized, various real‐world issues solved, evaluation quality utility images. We finally summarize challenges, propose potential solutions, highlight promising directions future research, aim providing guidance upcoming research.

Language: Английский

Enhanced super-resolution generative adversarial network augmented convolution neural network for pneumonia prognosis in India: promising health policy implications DOI
Tapan Kumar,

R. L. Ujjwal

International Journal of Systems Assurance Engineering and Management, Journal Year: 2025, Volume and Issue: unknown

Published: April 6, 2025

Language: Английский

Citations

0
Generative artificial intelligence in medical imaging: Current landscape, challenges, and future directions DOI Creative Commons
Wenle He,

Xuewei Wu,

Zhe Jin

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

Abstract The clinical significance, digital attributes, and underlying high‐dimensional information in medical images make them a key area for the artificial intelligence (AI) revolution health care. Generative AIs (GAIs) provide unprecedented abilities synthesizing diverse accurate simulated AI model training as well personalized disease management. However, several hurdles must be overcome prior to implementation, such biases introduced during synthesized risk of research falsification. This review outlines current landscape image synthesis through GAIs, with specific focus on variety synthesized, various real‐world issues solved, evaluation quality utility images. We finally summarize challenges, propose potential solutions, highlight promising directions future research, aim providing guidance upcoming research.

Language: Английский

Citations

0