Generative AI in Medicine: Emerging Applications, Challenges, and Future Directions
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 90
Special Issue Editor
Interests: deep learning; medical imaging; lung cancer; gastric cancer
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in generative artificial intelligence (generative AI), such as diffusion models, generative adversarial networks (GANs), and large language models (LLMs), have opened new frontiers in medical imaging, diagnostics, treatment planning, and patient communication. These technologies enable the synthesis of realistic medical images, the generation of clinical reports, and the simulation of rare disease scenarios, providing novel solutions to longstanding problems in healthcare.
This Special Issue aims to explore cutting-edge applications and developments in the field of generative AI for medicine. We welcome submissions that present novel algorithms, clinical applications, benchmarking studies, interpretability and safety frameworks, or ethical considerations related to the deployment of generative models in clinical practice.
Topics of interest include, but are not limited to:
- Medical image synthesis and enhancement
- Generative models for clinical report generation
- Multi-modal generative AI for radiology, pathology, and genomics
- Generative AI in medical education and training
- Evaluation, reliability, and explainability of generative outputs
- Regulatory and ethical issues in clinical deployment
We invite researchers and developers working at the intersection of AI and healthcare to contribute to this Special Issue and help shape the future of generative technologies in medicine.
Prof. Dr. Atsushi Teramoto
Guest Editor
Manuscript Submission Information
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Keywords
- generative AI
- medical image synthesis
- diffusion models
- report generation
- large language models (LLMs)
- clinical applications
- deep learning
- explainability
- AI ethics in healthcare
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