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. Recent studies have shown promising results in enhancing diagnostic accuracy and treatment outcomes through AI, yet there remain significant gaps in the seamless integration of these technologies into clinical practice. Current debates focus on the ethical implications, data privacy concerns, and the need for robust, context-aware AI models that can adapt to the dynamic nature of healthcare environments. Addressing these issues requires a concerted effort to develop innovative solutions that bridge the gap between technological advancements and practical healthcare applications.
This Research Topic aims to explore innovative applications of foundation models in healthcare, with a particular focus on Generative AI, computer vision, and natural language processing. The main objectives include demonstrating how these technologies can enhance diagnostic accuracy, improve treatment outcomes, and optimize healthcare operations. Specific questions to be addressed include: How can generative models create synthetic datasets that enhance data availability and privacy? What are the best practices for integrating language and vision models to generate accurate clinical reports? How can zero-shot learning be effectively applied in healthcare to ensure robust model performance without extensive task-specific data?
To gather further insights into the integration of AI in healthcare, we welcome articles addressing, but not limited to, the following themes:
- Image Analysis from Macro to Nano: exploring foundation models for medical and biomedical image analysis in radiology and microscopy.
- Generative AI and Synthetic Data: applying efficient generation models to create synthetic datasets for medical analysis, enhancing data availability and privacy.
- Language–Vision Integration: utilizing data-driven, context-aware rendering techniques for optimizing language–vision AI to generate accurate clinical reports.
- Zero-Shot Learning: demonstrating zero-shot learning in healthcare for robust model performance without extensive task-specific data.
- Novel Evaluation Metrics and Benchmarks: introducing metrics inspired by holistic frameworks to evaluate the effectiveness and reliability of AI in medical contexts.
- Technological Integration: implementing federated learning for secure, decentralized healthcare data analysis.
- Model Drift and Monitoring: offering strategies for model drift detection and management, informed by the latest benchmarking methodologies.
This Research Topic aims to explore innovative applications of foundation models in healthcare, with a particular focus on Generative AI, computer vision, and natural language processing. The main objectives include demonstrating how these technologies can enhance diagnostic accuracy, improve treatment outcomes, and optimize healthcare operations. Specific questions to be addressed include: How can generative models create synthetic datasets that enhance data availability and privacy? What are the best practices for integrating language and vision models to generate accurate clinical reports? How can zero-shot learning be effectively applied in healthcare to ensure robust model performance without extensive task-specific data?
To gather further insights into the integration of AI in healthcare, we welcome articles addressing, but not limited to, the following themes:
- Image Analysis from Macro to Nano: exploring foundation models for medical and biomedical image analysis in radiology and microscopy.
- Generative AI and Synthetic Data: applying efficient generation models to create synthetic datasets for medical analysis, enhancing data availability and privacy.
- Language–Vision Integration: utilizing data-driven, context-aware rendering techniques for optimizing language–vision AI to generate accurate clinical reports.
- Zero-Shot Learning: demonstrating zero-shot learning in healthcare for robust model performance without extensive task-specific data.
- Novel Evaluation Metrics and Benchmarks: introducing metrics inspired by holistic frameworks to evaluate the effectiveness and reliability of AI in medical contexts.
- Technological Integration: implementing federated learning for secure, decentralized healthcare data analysis.
- 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.
