To the Editor:Artificial intelligence(AI)is revolutionizing the biomedical field by enabling advanced data analysis,predictive modeling,and personalized medicine,driving breakthroughs in diagnosis,treatment,and drug d...To the Editor:Artificial intelligence(AI)is revolutionizing the biomedical field by enabling advanced data analysis,predictive modeling,and personalized medicine,driving breakthroughs in diagnosis,treatment,and drug discovery.In pursuit of this goal,researchers are attempting to develop AI-based algorithms and establish models for use in clinical settings.Key challenges in this pursuit include ensuring the models’accuracy and consistency and addressing issues such as the interpretability of AI decisions,integration into existing clinical workflows,and ethical considerations like data privacy.Additionally,the AI model lies in the quality and diversity of training data—robust models require diverse and representative datasets to ensure generalizability across different patient populations,reduce dependence on extensive labeled data,and remain resilient to domain shifts,enabling adaptation to new and unseen cases.Nevertheless,this field continues to grow,especially in image-based AI models for diagnosing diseases,such as cardiovascular diseases.展开更多
基金supported by the Sichuan Natural Science Foundation Outstanding Youth Science Foundation(No.2024NSFJQ0053)the National Natural Science Foundation of China(No.82370235)+1 种基金the Tianfu Qingcheng Plan(No.1711)the K-funding of West China Second University Hospital Sichuan University(No.KZ197).
文摘To the Editor:Artificial intelligence(AI)is revolutionizing the biomedical field by enabling advanced data analysis,predictive modeling,and personalized medicine,driving breakthroughs in diagnosis,treatment,and drug discovery.In pursuit of this goal,researchers are attempting to develop AI-based algorithms and establish models for use in clinical settings.Key challenges in this pursuit include ensuring the models’accuracy and consistency and addressing issues such as the interpretability of AI decisions,integration into existing clinical workflows,and ethical considerations like data privacy.Additionally,the AI model lies in the quality and diversity of training data—robust models require diverse and representative datasets to ensure generalizability across different patient populations,reduce dependence on extensive labeled data,and remain resilient to domain shifts,enabling adaptation to new and unseen cases.Nevertheless,this field continues to grow,especially in image-based AI models for diagnosing diseases,such as cardiovascular diseases.
