10076467Improved biomedical data harmonisation, the cornerstone of trustworthy and responsible AI in HealthcareClosedGrant for R&DInnovate UKA 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.