Generative and Multimodal Artificial Intelligence in Health Care: Opportunities, Challenges, and Future Directions

Abstract

Generative and multimodal artificial intelligence (AI) have revolutionized medical research and clinical practice by enabling deep integration of data across imaging, text, genomics, and clinical records. This review explores the current state of AI-driven technologies in healthcare, focusing on diagnostic imaging, genomics, precision medicine, workflow automation, and drug discovery. The article highlights how generative AI models—such as large language models (LLMs) and transformer-based architectures—are being deployed in real-world clinical environments to enhance decision-making, reduce human error, and accelerate biomedical innovation. However, challenges remain in terms of data quality, algorithmic bias, explainability, and ethical governance. The review concludes that while AI’s potential to transform medicine is unprecedented, its successful adoption requires robust validation, interdisciplinary collaboration, and strict regulatory oversight to ensure safety, accountability, and equity.