A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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A survey of synthetic data generation methods for deep learning in computer vision and medical imaging, with emphasis on reducing data bias.
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This paper examines how foundation models are shifting biomedical imaging from task-specific models to unified backbones, and what it will take to turn the hype into clinical reality.
Sarkar Siddique, James C. L. Chow
This topical review highlights how machine learning and AI applications in healthcare communication, including chatbots for COVID-19 education, cancer therapy, and medical imaging, can benefit humans.
Aurélie Pahud de Mortanges, Haozhe Luo, Shelley Zixin Shu, et al.
This paper reviews XAI for multimodal and longitudinal medical imaging data and proposes an XAI orchestrator to help clinicians synthesize data, predictions, and explanations.
Georgios Kaissis, Marcus R. Makowski, Daniel Rückert, et al.
This paper reviews federated, secure, and privacy-preserving AI methods for medical imaging, addressing data protection and utilization challenges.
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This paper surveys the status, challenges, and future directions of foundation models in medical imaging, contextualizing their shift from task-specific models.
Sungwon Lee, Joon-Yong Jung, Akaworn Mahatthanatrakul, et al.
This review evaluates AI's impact on spinal imaging and care, covering image quality, diagnosis, surgical planning, and personalized predictions.