在药物临床试验中，安全性评估与疗效评估分别扮演着重要且不同意义的角色。然而，以心脏磁共振成 像(CMR)为代表的传统影像学工具，受限于空间分辨率不足、定量指标单一和缺乏组织特异性，常在早 期漏检亚临床心脏毒性或细微功能变化，使高危受试者被误判为低风险。该盲区易使研究团队低估药物 的心脏毒性，从而错估获益-风险比，最终影响方案修订、开发决策及受试者后续管理。 PART.01 AI助力心血管疾病风险评估，多模态数据融合成关键 当前临床实践中，肥厚型心肌病的诊断准确率仅50%左右，这一局限性导致医生在评估患者是否需要植 入心脏除颤器等预防性治疗时面临重大决策困境。2025年7月2日，约翰·霍普金斯大学的研究团队在 Nature Cardiovascular Research 发表题为"Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy"的研究。 MAARS是一款功能强大且值得信赖的临床决策支持工具。它有可能极大改善肥厚型心肌病(HCM)患者 的临床决策和医疗护理，可通过自动化数据提取系统直接整合至临床流程，也可作为辅助工 ...

Group 1: Policy Support for Innovative Drug Development - The National Healthcare Security Administration and the National Health Commission issued measures to support high-quality development of innovative drugs, proposing 16 measures across five areas to enhance the entire chain of innovative drug research, access, hospital use, and diversified payment [1][2] - The measures aim to achieve the goals of "true support for innovation, support for genuine innovation, and support for differentiated innovation" [1] Group 2: Current Status of Innovative Drug Development - The number of approved Class I innovative drugs in China has shown a significant upward trend, with 48 approvals expected in 2024, which is over five times the number in 2018, and nearly 40 approvals already in the first half of this year [2] - The emergence of innovative drugs has optimized the clinical medication structure and significantly improved medication security for the public, as evidenced by the increase in the five-year survival rate of cancer patients from 33.3% a decade ago to 43.7% in 2023, saving approximately 500,000 lives annually [2] - Challenges include intensified homogenization competition and a gap between the pricing expectations of innovative drug companies and the payment capabilities of health insurance, affecting the accessibility of innovative drugs [2] Group 3: AI Applications in Innovative Drug Development - Rapidly developing AI technology can address some challenges in innovative drug development by accurately predicting drug molecule activity, selectivity, and toxicity, thus accelerating drug discovery and reducing costs [3] - Insilico Medicine utilized Generative Adversarial Networks (GAN) technology to design a candidate drug for idiopathic pulmonary fibrosis, completing preclinical research in just 18 months compared to the traditional 4 to 5 years [3] Group 4: AI in Identifying Potential Drug Targets - AI's data processing capabilities allow for the analysis of vast biomedical data, identifying complex patterns and potential relationships that humans may overlook, thus discovering new drug targets [4] - GATC Health's AI drug development platform integrates disease-specific data and proprietary AI solutions to identify promising targets early in the drug development process [4] - Genomenon employs Genome Language Processing (GLP) technology to extract and standardize genomic and clinical information from extensive literature, facilitating drug development and rare disease diagnosis [4] Group 5: AI Enhancing Clinical Research Efficiency - AI-driven solutions provided by companies like Taimei Zhiyuan enhance clinical research efficiency through end-to-end services, including patient recruitment and data management [5] - The company has developed various intelligent platforms to improve the quality and success rate of clinical trials, responding to the call for strengthening real-world research on innovative drugs [5] Group 6: Future Outlook for China's Innovative Drug Industry - With the dual empowerment of policy support and AI technology, China's innovative drug industry is expected to enter a higher quality development phase [6] - Companies are encouraged to leverage AI technology to enhance research efficiency and align with the policy direction of "differentiated innovation," contributing to China's transition from a major pharmaceutical country to a strong pharmaceutical nation [6]