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1.
《Value in health》2022,25(3):331-339
ObjectivesClinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods.MethodsWe used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use.ResultsWe found that the measurement of core elements of value—health outcomes and cost—are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians’ productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians’ workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians’ work can improve productivity but may reduce skills.ConclusionsThe potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value. 相似文献
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《Drug discovery today》2022,27(1):82-101
WNT/β-catenin signaling orchestrates various physiological processes, including embryonic development, growth, tissue homeostasis, and regeneration. Abnormal WNT/β-catenin signaling is associated with various cancers and its inhibition has shown effective antitumor responses. In this review, we discuss the pathway, potential targets for the development of WNT/β-catenin inhibitors, available inhibitors, and their specific molecular interactions with the target proteins. We also discuss inhibitors that are in clinical trials and describe potential new avenues for therapeutically targeting the WNT/β-catenin pathway. Furthermore, we introduce emerging strategies, including artificial intelligence (AI)-assisted tools and technology-based actionable approaches, to translate WNT/β-catenin inhibitors to the clinic for cancer therapy. 相似文献
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王茹王毓张婷婷汪超 《中国卫生质量管理》2022,(7):080-85
目的降低重症患者人工气道湿化不足发生率。方法开展品管圈活动,通过医护双方合作,优化气道管理团队,完善人工气道湿化评估方案,改良气道湿化装置,增加气道湿化途径,实行复合序贯式湿化-排痰护理管理模式等,实现了气道湿化、辅助排痰、气道廓清一体化。结果重症患者人工气道湿化不足发生率从28.57%降低至11.26%,目标达标率为100.58%,进步率为60.59%。结论通过开展品管圈活动,规范了重症患者人工气道湿化方案,降低了气道相关并发症发生率,有效改善了患者治疗结局,提升了医疗质量。 相似文献
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《Clínica e investigación en ginecología y obstetricia》2022,49(1):100709
The prevalence of infertility among the population of reproductive age continues to increase year after year in Western societies and the therapeutic resource in most cases will be assisted reproductive techniques, mainly in vitro fertilization. Although the advances made in vitro fertilization have had an undeniable impact on the chances of achieving pregnancy, the efficiency of the technique, in global terms, remains low. One of the factors with the greatest impact on in vitro fertilization results is embryo selection. That is why among the main future challenges that arise in the field of assisted human reproduction, is that of obtaining reliable embryo selection methods that allow the transfer of the embryo with greater competence to implant. In this article, the different embryo selection methods used in in vitro fertilization are reviewed. 相似文献
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目的 探索人工智能骨髓细胞识别系统Morphogo应用于多发性骨髓瘤微小残留病(minimal residual disease,MRD)检测的临床价值及面临问题。方法 收集已经由流式细胞术(multiparameter flow cytometry,MFC)检查后明确微小残留病结果的病例65例,调取其留存的骨髓瑞氏染色涂片,通过基于人工智能(artificial intelligence,AI)平台的分析系统Morphogo对所有骨髓涂片进行全自动扫描及细胞分类。AI及细胞形态学多发性骨髓瘤MRD阳性阈值设为浆细胞比例大于4.4%。按AI自动识别细胞数量将病例分为I 500组、I 1000组、I 2000组,每组病例的人工智能微小残留病(AI-MRD)、细胞形态学(morphology)微小残留病(M-MRD)和流式细胞术微小残留病(MFC-MRD)结果两两行Kappa一致性检验,并计算各组敏感度、特异度、准确度。分别以MFC-MRD和M-MRD结果为金标准绘制AI-MRD的受试者工作特征(receiver operating characteristic,ROC)曲线并计算其曲线下面积(area under the curve,AUC)。结果 分组后AI-MRD vs. MFC-MRD 和AI-MRD vs. M-MRD的Kappa值、敏感度、特异度、准确度、AUC均随识别细胞数量的增加而增高,其中I 2000组AI-MRD vs. MFC-MRD的Kappa一致性检验结果为Kappa=0.500(P=0.013),敏感度为71%,特异度为80%,准确度为75%;AI-MRD vs. M-MRD的Kappa一致性检验结果为Kappa=0.667(P=0.001),敏感度为100%,特异度为75%,准确度为83%。以MFC-MRD结果为标准,I 2000组AI-MRD的ROC AUC=0.800(P=0.002,95%CI=0.588~0.934),M-MRD的ROC AUC=0.779(P=0.005,95%CI=0.564~0.921)。结论 人工智能骨髓细胞识别系统Morphogo检测多发性骨髓瘤MRD具有细胞识别准确度高、速度快、成本低等特点,后续开发中应加入细胞组化染色、细胞免疫等技术提高人工智能多发性骨髓瘤微小残留病诊断的准确率。 相似文献
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《Clinical oncology (Royal College of Radiologists (Great Britain))》2022,34(2):e97-e103
Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches. 相似文献
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随着人类基因组测序、生物大数据信息分析、分子病理检测和人工智能辅助病理诊断等技术进步及其应用, 临床医学发展迈向精准诊疗时代。这一时代背景下, 传统诊断病理学迎来前所未有的历史机遇, 正在向"下一代诊断病理学(next-generation diagnostic pathology)"迈进。下一代诊断病理学以病理形态和临床信息为诊断基础, 以分子检测与生物信息分析、智慧制样与流程质控、智能诊断与远程会诊、病灶活体可视化与"无创"病理诊断等创新前沿交叉技术为主要特征, 以多组学和跨尺度整合诊断为病理报告内容, 实现对疾病的"最后诊断", 并预测疾病演进和结局、建议治疗方案和评估治疗反应, 形成新的疾病诊断"金标准"。未来, 需要激发病理学科创新活力, 加快下一代诊断病理学成熟和应用, 重塑病理学科理论和技术体系, 发挥诊断病理学在疾病"防、诊、治、养"等过程中的重要作用, 促进临床医学进一步发展, 服务健康中国战略。 相似文献