The Healthcare Research Programme have recently published a white paper on the topic of AI in healthcare, titled – How do I turn this on? What to consider when adopting AI-based tools into clinical practice.
On the back of this paper, we are exploring how we can support healthcare stakeholders in understanding the AI adoption process and addressing some of the considerations we proposed.
DNV is involved in a Nordic Innovation project, two Horizon 2020 research projects and a NFR funded PhD all aimed at using artificial intelligence to improve health outcomes.
FederatedHealth aims to harness the potential of unstructured data in Electronic Health Record (EHR) systems by developing a federated health data network in the Nordics, using distributed machine learning to ensure data privacy while processing multilingual clinical text from several languages. DNV is leading the identification and analysis of barriers for implementing the federated learning approach and mapping solutions to overcome these. DNV is also assessing the data and model security chosen for the federated learning infrastructure.
REALMENT aims to bring personalized medicine interventions to psychiatry, by developing a real-world data platform and a clinical management platform to improve and optimize treatment of mental disorders through novel artificial intelligence and machine learning tools. DNV is leading exploratory activities identifying and analyzing trust needs for safety, security and transparency as well as assessing regulatory requirements and guidelines to enable lawful and ethical access to data sets and data sharing for further data exploitation by independent parties.
AI-MIND aims to reduce the burden of dementia by developing novel, AI-based tools to support healthcare professionals in predicting dementia, thus enabling earlier interventions for patients. DNV’s involvement includes, delivering a guideline for legal and ethical data processing; developing a framework for data governance and data management framework; designing and implementing a data model; and developing and implementing methods for continuous data quality assurance.
In an NFR funded PhD project, DNV with partners Oslo University Hospital, the Cancer Registry of Norway and the University of Oslo is exploring how the use of synthetic data for AI development and validation can be assessed to ensure safe implementation of AI in healthcare. As healthcare data, with its inherently sensitive nature, is often challenging to share and process, synthetic data is increasingly seen as a practical way to speed up the development process while protecting patient privacy. The project will also investigate reidentification risk and their legal consequences for different use case scenarios.