Large AI Models in Life Sciences and Healthcare: A Review and Analysis
TL;DR
Large AI models are reshaping biomedical research and healthcare delivery by promoting more intelligent, data-driven, and integrated approaches by demonstrating superior capabilities in knowledge representation, reasoning, and cross-domain learning.
Abstract
Recent advances in large artificial intelligence (AI) models have transformed the landscape of life sciences and healthcare by enabling more efficient knowledge extraction, biomedical data analysis, and clinical decision support. Compared with traditional task-specific AI models, large language models (LLMs), generative AI, and multimodal foundation models demonstrate superior capabilities in knowledge representation, reasoning, and cross-domain learning. This review summarizes recent developments in the application of large AI models in life sciences and healthcare. First, the technical foundations of large AI models and their adaptation to biomedical data are introduced. Subsequently, representative applications in life science research, clinical healthcare, and drug discovery are reviewed, with particular attention to biomedical literature analysis, biological sequence modeling, medical image interpretation, clinical decision support, and precision medicine. Furthermore, the opportunities and challenges associated with deploying large AI models in healthcare are discussed, including improvements in research efficiency, disease diagnosis, and personalized treatment, as well as concerns regarding hallucination, privacy protection, algorithmic bias, interpretability, and regulatory governance. Overall, large AI models are reshaping biomedical research and healthcare delivery by promoting more intelligent, data-driven, and integrated approaches. Future progress will depend on continuous advances in trustworthy AI, multimodal learning, and interdisciplinary collaboration to ensure safe, reliable and clinically applicable AI systems.