About this Research Topic
Artificial Intelligence (AI) has undergone remarkable advancements, revolutionizing fields such as general computer vision and natural language processing. These technologies, integral to the broader capabilities of AI, are increasingly relevant in the healthcare sector, particularly through the application of Generative AI and multimodal systems. Despite the potential of AI to significantly impact medical imaging and healthcare outcomes, its full integration faces unique challenges posed by the complex nature of medical and biomedical data.
This Research Topic invites contributions that explore innovative applications of foundation models in healthcare, with a particular focus on Generative AI, computer vision, and natural language processing. Potential authors are encouraged to submit original research that demonstrates how these technologies can enhance diagnostic accuracy, improve treatment outcomes, and optimize healthcare operations.
Submissions at the intersection of AI and healthcare are welcome, especially those focusing on efficient generative models and data-driven approaches, context-aware synthesis, and holistic frameworks. Specifically, this Research Topic seeks to highlight how these computer vision advancements can enhance radiological and oncological diagnostics and treatments, bridging technology with clinical applications. Contributions should demonstrate practical healthcare innovations using vision, language, or multi-modal AI models.
Topics of interest include, but are not limited to:
1. Image Analysis from Macro to Nano: exploring foundation models for medical and biomedical image analysis in radiology and microscopy.
2. Generative AI and Synthetic Data: applying efficient generation models to create synthetic datasets for medical analysis, enhancing data availability and privacy.
3. Language–Vision Integration: utilizing data-driven, context-aware rendering techniques for optimizing language–vision AI to generate accurate clinical reports.
4. Zero-Shot Learning: demonstrating zero-shot learning in healthcare for robust model performance without extensive task-specific data.
5. Novel Evaluation Metrics and Benchmarks: introducing metrics inspired by holistic frameworks to evaluate the effectiveness and reliability of AI in medical contexts.
6. Technological Integration: implementing federated learning for secure, decentralized healthcare data analysis.
7. Model Drift and Monitoring: offering strategies for model drift detection and management, informed by the latest benchmarking methodologies.
This Research Topic invites contributions that explore innovative applications of foundation models in healthcare, with a particular focus on Generative AI, computer vision, and natural language processing. Potential authors are encouraged to submit original research that demonstrates how these technologies can enhance diagnostic accuracy, improve treatment outcomes, and optimize healthcare operations.
Submissions at the intersection of AI and healthcare are welcome, especially those focusing on efficient generative models and data-driven approaches, context-aware synthesis, and holistic frameworks. Specifically, this Research Topic seeks to highlight how these computer vision advancements can enhance radiological and oncological diagnostics and treatments, bridging technology with clinical applications. Contributions should demonstrate practical healthcare innovations using vision, language, or multi-modal AI models.
Topics of interest include, but are not limited to:
1. Image Analysis from Macro to Nano: exploring foundation models for medical and biomedical image analysis in radiology and microscopy.
2. Generative AI and Synthetic Data: applying efficient generation models to create synthetic datasets for medical analysis, enhancing data availability and privacy.
3. Language–Vision Integration: utilizing data-driven, context-aware rendering techniques for optimizing language–vision AI to generate accurate clinical reports.
4. Zero-Shot Learning: demonstrating zero-shot learning in healthcare for robust model performance without extensive task-specific data.
5. Novel Evaluation Metrics and Benchmarks: introducing metrics inspired by holistic frameworks to evaluate the effectiveness and reliability of AI in medical contexts.
6. Technological Integration: implementing federated learning for secure, decentralized healthcare data analysis.
7. Model Drift and Monitoring: offering strategies for model drift detection and management, informed by the latest benchmarking methodologies.
Keywords: healthcare, medical imaging, foundation models, multimodal learning, natural language processing (NLP), imaging, oncology, radiology, pathology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
