Chen Haohao, Zeng Ying, Cai De
Department of Pharmacy, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Department of Pharmacology, Shantou University Medical College, Shantou, China.
Front Artif Intell. 2025 Sep 1;8:1580445. doi: 10.3389/frai.2025.1580445. eCollection 2025.
Cardiovascular diseases (CVDs) remain the leading causes of morbidity, mortality, and healthcare expenditures, presenting substantial challenges for hospitals operating under Diagnosis-Related Group (DRG) payment models. Recent advances in deep learning offer new strategies for optimizing CVD management to meet cost control objectives. This review synthesizes the roles of deep learning in CVD diagnosis, treatment planning, and prognostic modeling, emphasizing applications that reduce unnecessary diagnostic imaging, predict high-cost complications, and optimize the utilization of critical resources like ICU beds. By analyzing medical images, forecasting adverse events from patient data, and dynamically optimizing treatment plans, deep learning offers a data-driven strategy to manage high-cost procedures and prolonged hospital stays within DRG budgets. Deep learning offers the potential for earlier risk stratification and tailored interventions, helping mitigate the financial pressures associated with DRG reimbursements. Effective integration requires multidisciplinary collaboration, robust data governance, and transparent model design. Real-world evidence, drawn from retrospective studies and large clinical registries, highlights measurable improvements in cost control and patient outcomes; for instance, AI-optimized treatment strategies have been shown to reduce estimated mortality by 3.13%. However, challenges-such as data quality, regulatory compliance, ethical issues, and limited scalability-must be addressed to fully realize these benefits. Future research should focus on continuous model adaptation, multimodal data integration, equitable deployment, and standardized outcome monitoring to validate both clinical quality and financial return on investment under DRG metrics. By leveraging deep learning's predictive power within DRG frameworks, healthcare systems can advance toward a more sustainable model of high-quality, cost-effective CVD care.
心血管疾病（CVDs）仍然是发病、死亡和医疗保健支出的主要原因，给在诊断相关分组（DRG）支付模式下运营的医院带来了巨大挑战。深度学习的最新进展为优化心血管疾病管理以实现成本控制目标提供了新策略。本综述综合了深度学习在心血管疾病诊断、治疗规划和预后建模中的作用，重点强调了减少不必要的诊断性成像、预测高成本并发症以及优化ICU床位等关键资源利用的应用。通过分析医学图像、根据患者数据预测不良事件以及动态优化治疗计划，深度学习提供了一种数据驱动的策略，以在DRG预算范围内管理高成本程序和延长的住院时间。深度学习具有实现早期风险分层和量身定制干预措施的潜力，有助于减轻与DRG报销相关的财务压力。有效的整合需要多学科合作、强大的数据治理和透明的模型设计。来自回顾性研究和大型临床登记处的真实世界证据突出了在成本控制和患者结局方面可衡量的改善；例如，人工智能优化的治疗策略已被证明可将估计死亡率降低3.13%。然而，必须解决数据质量、法规遵从性、伦理问题和可扩展性有限等挑战，以充分实现这些益处。未来的研究应专注于持续的模型适应性、多模态数据整合、公平部署和标准化结局监测，以根据DRG指标验证临床质量和投资财务回报。通过在DRG框架内利用深度学习的预测能力，医疗保健系统可以朝着更可持续的高质量、具有成本效益的心血管疾病护理模式迈进。
