Improved biomedical data harmonisation, the cornerstone of trustworthy and responsible AI in Healthcare

阻碍人工智能在医疗保健领域发展的一个关键问题是它的可信度。人工智能有可能给医疗保健和医学带来革命性的变化，实现更快、更准确的诊断、个性化的治疗计划和更高效的医疗系统。然而，在医疗保健和医疗领域采用人工智能也带来了挑战，特别是在确保这些系统的可信性方面。值得信赖的人工智能对于将人工智能成功地整合到健康环境中至关重要，因为它确保了人工智能系统的可靠性、透明度和问责制。数据对于人工智能开发是必不可少的，因为它是支持ML算法的燃料。训练数据集的偏差和缺乏可解释性将限制这类技术的有效性。偏差是通过各种因素引入的，包括研究设置、数据收集方法和用于准备数据的算法。所有这些都会导致不准确的预测和诊断，给患者带来潜在的有害后果。因此，减少训练数据集的偏差对于在医疗领域建立可信的人工智能系统至关重要。生物医学部门正在努力解决这些问题，但协调来自不同来源的核心生物医学数据往往会阻碍项目的推进。产品开发和ML团队经常被阻止使用更大的协调数据集，因为将不同来源的生物医学数据整合在一起往往并不容易。该项目将使新成立的公司能够弥合关键的商业化差距，并获得一个有效的演示器，用于对应用程序开发人员感兴趣的关键数据资源的协调步骤进行海市化和评估，作为滩头阵地市场切入点。该项目的根源是人工智能模型对特定人群的不公平和不准确。我们的创新将有助于确保对全球AI/ML市场产生公平的影响，而不受地理、历史、文化和收入的影响。公共资金将用于弥合商业化差距，并使外来投资能够在一个新的英国企业中获利，以利用一个新兴的高增长行业。

A key problem holding back AI in healthcare is its trustworthiness. AI has the potential to revolutionise healthcare and medicine, enabling faster and more accurate diagnoses, personalised treatment plans, and more efficient healthcare systems. However, the adoption of AI in healthcare and medicine comes with challenges, particularly in ensuring the trustworthiness of these systems. Trustworthy AI is essential for the successful integration of AI into health settings, as it ensures the reliability, transparency, and accountability of AI systems.Data is essential for AI development as it is the fuel powering the ML algorithms. Bias in the training datasets and the lack of interpretability will limit the effectiveness of such technologies. Biases are introduced through a variety of factors, including the study setup, the data collection methods, and the algorithms used to prepare the data. All these result in inaccurate predictions and diagnoses, leading to potentially harmful consequences for patients. Reducing the bias of training datasets is therefore critical to building trustworthy AI systems in healthcare.The biomedical sector is trying to solve these issues, but more often than not, harmonising core biomedical data from different sources blocks projects moving forward. Product development and ML teams are often held back from using larger harmonised datasets because it is often not easy to bring different sources of biomedical data together.The project will enable the newly formed company to bridge a key commercialisation gap and obtain a working demonstrator for hamarising and evaluating the harmonisation steps for key data resources of interest to application developers, as a beachhead market entry point.The project's genesis is the inequity and inaccuracy of AI models to specific population groups. Our innovation will help to ensure an equitable impact on the global AI/ML market, regardless of geography, history, culture and income.The public funding will be used to bridge the commercialisation gap and enable inward investment in a new UK venture to capitalise on an emerging high-growth sector.