Editorial: Foundation Models for Healthcare: Innovations in Generative AI, Computer Vision, Language Models, and Multimodal Systems

Sokratis Makrogiannis^*

• Delaware State University, Dover, United States

Second, large language models are emerging as practical tools to generate realistic synthetic clinical data. to generate perioperative tabular datasets and found that most parameters' distributions were statistically similar to an open real dataset, suggesting LLM-based synthetic data could alleviate privacy and access bottlenecks for secondary analyses and method development. However, synthetic realism does not automatically equate clinical utility or bias-free data; rigorous validation is still required. • Clinical validation pathways: Fund and run prospective trials and real-world deployments (not only retrospective benchmarks) to verify clinical value and safety. • Explainability & human-in-the-loop design: Integrate clinicians in the loop and deploy explainability tools that matter for decision-making and error detection. The Frontiers Research Topic brings together work that illustrates both the promise and the complexity of applying foundation models in healthcare. From zero-shot microscopy segmentation to LLM-driven synthetic data generation and multimodal prognostic systems, the field is moving rapidly. The path to clinical applications requires rigorous validation, improved evaluation frameworks, and multidisciplinary coordination among AI researchers, clinicians, ethicists, and regulators. The papers in this Topic are a valuable step forward and provide concrete starting points for the coordinated effort needed to translate foundation models into safe, equitable, and useful clinical tools.