Over the past decade, artificial intelligence (AI) has become increasingly important as a disrupter in the future of medicine. Big data and advances in technology are driving opportunities for the application of AI and machine learning (ML) in health care and clinical decision-making at an unprecedented pace.
由于其在数据生成中的作用，体外诊断（IVD）实验室位于临床决策的中心。最近的研究on the awareness of AI in the U.S. clinical laboratory, conducted by theIQVIA™BBC IVD解决方案team, showed that most laboratory professionals have already seen some advances driven by AI in their laboratory. As a result, they expect dramatic changes within the next two to five years in both the laboratory landscape and their routine workflow.
IQVIA团队在核心微生物学，分子和病理学环境中与87个美国实验室进行了交谈，以了解IVD制造商的需求，关注和期望，因为AI和ML的进步破坏了他们当前的流程。
打击关注和建立潜力
Uncertainties, including reliability, timing, patient safety and financing, remain around the feasibility of implementation and oversight of AI solutions. In the IQVIA survey, noticeable numbers of respondents were hesitant to be an early adopter of AI/ML applications focused on clinical decision. In contrast, just under60％参与者几乎无关紧要，与AI/ML的潜在影响有关。相反，他们将AI视为实验室技术发展的下一步，可以减少人为错误，并为最佳患者疗法提供客观的指导。
On average,87％of participants predicted that AI could have a somewhat dramatic to very dramatic impact on current diagnostic testing processes. Cost savings emerged as the most influential factor supporting the adoption of AI applications, primarily as a byproduct of reducing or replacing human labor.
Still, significant concerns exist over quality, the potential for medical errors, misdiagnosis, gaps in training of AI users, and the reliability of the AI results (e.g., “black box”). In addition, electronic medical record (EMR) and electronic health record (EHR) systems were cited as not advanced enough to deal with AI at the moment. Respondents also named upfront costs as the primary inhibitor for adoption. These included the price of the applications themselves, as well as the significant investment needed in initial training and service costs. Other important constraints included concerns surrounding FDA/regulatory approval, data security, patient safety concerns and confusion around reimbursement.
必威手机APP医疗保健专业人员（HCP）不能忽略基于AI的解决方案所带来的可能性。几乎50％of survey participants cited operational efficiency as the area of greatest immediate potential for AI applications. Within the walls of the laboratory, operational efficiency means faster turnaround time for tests, automating repetitive tasks, improving diagnostic test utilization, etc. Other major areas of potential cited included clinical decision support and standardization of care. Tool advancements in the area of reflex testing, error detection and imaging analysis were considered the most easily achievable tasks.
Implications
没有人质疑AI将完全改变并破坏实验室，患者诊断和治疗以及主要利益相关者的作用：HCP，实验室人员和IVD制造商的角色。实验室仍然存在的问题是：
- How do we get from A to Z without putting patients at risk?
- 谁拥有数据？
- How do we control the data and the output?
- 这将如何受到监管？
- 如何支付和偿还？
实验室对他们与IVD制造商的未来关系及其在实施实验室实施AI/ML工具方面的作用很高。对于IVD制造商而言，这具有重大影响。这将意味着全新的职责和利益相关者，并且需要投资组合和服务的发展。由于需要通过结果数据来证明临床公用事业，以推动采用任何工具，因此现实世界的证据患者数据将是前进的关键。
AI also has the potential to finally address the shortage of laboratory staff/technicians that has plagued the field for years. Technology advancements have already begun to lead to the creation of new roles in data and IT (e.g., bioinformatics) for the application's upkeep and analytics. From a disease state perspective, respondents called out the opportunity to figure out more about complex conditions like cancer through clinical trial matching, and infectious disease through outbreak surveillance and prediction. By automating tasks and processing more data, AI will empower HCPs to find more meaningful solutions to both basic and complicated issues in health care.
AI工具有可能在空前的水平上推进患者护理，疾病管理，预防和治疗。尽管新闻媒体在当今的研究中继续报告了似乎轶事的突破，但实验室专业人员并不想知道是否何时以及AI如何在其工作流程中发挥重要作用。
要了解有关“美国实验室的人工智能意识和期望" study, please contact theiqvia团队。