临床试验市场的全球AI估计价值为: 2024年1.42亿美元 预计将达到 到2031年达到8.50亿美元, 以复合年增长率增长 (CAGR)从2024年到2031年占29.1%. 大赦国际有可能通过改进患者的招募和保留、试验设计、患者监测等等来优化临床试验。 临床研究越来越多地采用人工智能解决方案,预计在预测期间将促进市场增长。 临床试验市场中的AI预计将在未来几年出现大幅增长。 需要降低与临床试验相关的费用并提高效率,这促使人们更多地采用人工智能解决方案。 此外,政府支持将AI纳入医疗保健的举措和投资也在助长市场趋势。 高级算法正在各个领域帮助临床研究人员,包括病人的招募,药物发现和个性化治疗.

市场驱动力 - AI分析大数据集,改善患者特有结果的能力驱动的个性化医学日益增强的趋势.

近代以来,在个性化医学的日益增长趋势的带动下,保健行业发生了重大转变,根据个人病人特点和基因特征,对治疗和临床试验进行调整。 人工智能和机器学习技术的进步在很大程度上推动了这种范式转变,从而能够更有效地分析大量病人数据集。

配备了深层学习算法的AI系统现在可以以前所未有的规模来挖掘电子健康记录,基因剖面图,医疗图像和其他敏感的病人信息,以辨别微妙的规律和相关性. 这有助于向临床医生提供关于最有效疗法、药物反应以及特定基因特征或医学史副作用的可操作的见解。 一些人工智能特征分析工具正在加强临床研究,根据生物标记表达、疾病严重性和其他个性化参数,为征聘合适的病人群体进行试验提供便利。

此外,大赦国际正在通过模拟各种假设情况,寻找改进试验设计方面的应用。 这使研究人员能够以数据驱动的方式优化处理方法、终点选择和其他协议方面,以最大限度地实现试验成功概率以及参与者的成果和经验。 一些玩家还利用机器学习算法,将医学文献档案堆积起来,深入了解新的生物标记药物协会和不良事件模式,以帮助发现更安全和更有针对性的疗法。

随着对个性化保健的关注的扩大,临床试验领域的玩家承认AI和现实世界的数据是推动提供定制治疗途径趋势的关键推动因素。 展望未来,继续增强计算能力、数据可用性以及AI模型的可解释性,可望加强其为下一代精密医学试验提供动力的效用。

提高管理机构的认识

在过去几年中,管理机构开始认识到AI和现实世界数据在转变临床研究各个方面的潜力. 林业发展局和EMA等机构日益接受这些技术,为在临床试验领域更广泛地采用这些技术提供了必要的动力。

例如,当局广泛欢迎利用AI进行审判程序优化、病人招聘和监测。 安全报告还得益于AI应用程序,这些应用程序有助于更快地发现潜在的不利事件。 监管者还承认AI驱动的电子健康记录分析所产生的真实世界证据对于加快批准新迹象的价值。

最近,一些框架文件承认AI/ML工具是在今后审判中进行终点评估的可行选择。 大赦国际还被认为适合通过汇总各种数据来源来确保遵守协议。 这与早先对"黑盒"算法的犹豫不决形成对比. 然而,这种鼓励伴随着某些透明度、验证和文献规范。

利益攸关方是积极的,随着时间的推移,随着人工智能技术的进一步成熟,监管认可将包含更为复杂的应用,如基于人工智能的诊断工具和个性化临床决策支持系统。 总体而言,有利于AI的监管潮被视作提高临床试验全景区收养率的大驱动力. 它为公司围绕这些数据驱动的方法简化发展组合和业务提供了必要的支持。

市场挑战----由于不同的保健数据和监管差异,AI模型标准化方面的挑战

由于医疗保健数据的多样性和监管差异,AI模型标准化面临挑战.

全球AI在临床试验市场面临的主要挑战之一是AI模型缺乏标准化. 由于文件惯例、电子健康记录系统以及病人隐私条例的差异,来自不同国家和地区的医疗保健数据有多种不同格式。 这使得很难开发AI模型,能够无缝地分析来自多个全球位置的数据. 缺乏共同数据标准也抑制了AI算法的跨境验证和比较. 在将人工智能和现实世界患者数据用于医疗目的方面,不同的监管环境产生了进一步的挑战。 解决数据和规章方面的这些多样性,对于充分实现AI在支持全球临床试验方面的标准化潜力至关重要。

市场机会 - AI促进的超个性化医学和试验设计,提高治疗效果和试验结果。 AI具有分析大量患者数据的能力,具有解锁超人性化医学和临床试验设计的潜力. 通过利用生物标记、遗传信息、医疗历史等方面的模式,AI可以帮助确定针对极具特色的患者分组的具体治疗选择和最佳试验组群。 AI提供的这种精准程度可望大大提高治疗的功效和效果。 它还可以通过将资源更好地集中用于最可能受益的病人来缩短审判时间。 AI在全球促进更安全、更快和更有效的临床研究的机会,可以在未来几年内改变制药和保健部门。

By Offering - Demand for streamlining clinical trials drives software adoption

Software contributes the highest share of the Global AI in Clinical Trials market owing to the growing need for improving clinical trial efficiency and quality. Clinical trials are complex processes involving collaboration between research sites, patients and sponsors. Software platforms help integrate data from different sources and provide insights to streamline processes. They automate repetitive tasks like patient enrollment, site selection, protocol design, randomization and blinding. This frees up time for clinicians to focus on high-value activities.

Platforms such as trial management systems and electronic data capture solutions are seeing increased uptake. They enable remote monitoring of trials and ensure data integrity with features such as audit trails and version control. Software also powers applications for patient recruitment and retention. Chatbots and virtual assistants communicate trial details, manage schedules and address queries in a more personalized manner. This boosts participant engagement and compliance. Moreover, AI-based tools can match candidates to suitable trials based on profiles, reducing screening failures.

Adoption is further encouraged by regulations on electronic records and signatures. Software complies with standards such as 21CFRPart11 and provides audit trails as per International Council for Harmonisation guidelines. It replaces paper-based workflows while meeting all compliance requirements. The drive for decentralization amid the pandemic has accelerated digital transformation as well. Cloud-based platforms facilitate remote operations from patient recruitment to monitoring. This allows trials to continue seamlessly and helps sponsors evaluate virtual approaches for future studies.

 By Technology - Machine learning dominates driven by its capability in big data

Machine learning contributes the highest share in the By Technology segment due to its ability to leverage large and diverse datasets. The volume and complexity of clinical trial data is constantly increasing with addition of omics data, real-world evidence and patient-generated inputs. Machine learning algorithms can identify patterns across parameters and participant subgroups that are impossible to detect manually.

Deep neural networks power applications for vital sign monitoring, gene sequencing, drug discovery and more. They recognize anomalies, predict responses and recommend optimized treatment paths based on similarities with past cases. Approaches like convolutional neural networks even learn directly from medical images, eliminating manual feature extraction. Reinforcement learning automates trial simulations to propose better protocol designs. At the same time, unsupervised learning techniques organize heterogeneous data into meaningful subgroups for stratification and endotyping.

Compared to deep learning, machine learning requires less data for initial training and is more interpretable. Regulators prefer algorithms that can explain their outputs for review purposes. Approaches like decision trees, random forests and support vector machines meet these needs while delivering high performance. They are widely adopted for tasks such as predicting adverse events and treatment response using real-world data from electronic health records. Machine learning thus leads by offering scalable, explainable and customizable solutions.

 By Application- Significant disease burden drives Cardiovascular trials adoption of AI

Among applications, Cardiovascular contributes the highest share driven by rising cases of conditions like heart disease, stroke and hypertension. These illnesses have enormous social and economic consequences worldwide as reflected in growing healthcare costs. There is urgent need for innovative treatments and prevention strategies. AI can help by accelerating discovery and evaluation of new medicines and protocols through analysis of vast amounts of cardiovascular data.

Machine learning processes variables like biomarkers, family history, images and more to stratify heart disease subtypes more precisely for targeted therapies. It detects subtle changes in heart functioning from signals that are missed by humans. AI may also serve as virtual assistants for remote monitoring of patients on trials. This allows trials on lifestyle/behavioral interventions to include participants regardless of location. For conditions where early detection and treatment saves lives, AI can mine risk factors to identify high-risk groups for prevention studies.

Supervised learning on datasets from past clinical trials and real-world outcomes trains models for tasks like estimating treatment response variability more accurately. Such predictive analytics support sample size calculations and power analyses to design efficient cardiovascular studies. By streamlining operations through digital workflows and insights, AI helps sponsors evaluate promising solutions faster. This could significantly improve cardiovascular disease management and quality of life.

临床试验市场全球AI主要运营者包括Capricor治疗学,Codiak生物科学,Onco治疗科学,Bio-Techne,NanoFCM Inc.,系统生物科学,LLC,AcouSort AB,Aethlon Medical, Inc.,Everzom,Kimera Labs,ExoCoBio,MD保健,Thermo Fisher Science,浙江大学,加利福尼亚大学,Syngene和WACKER.