In this project, we are gathering lessons learned from key stakeholders in implementing AI into clinical practice. We are engaging with stakeholders and AI adoption pioneers who have been involved in or are starting to implement AI within specialist healthcare, primary healthcare or direct to consumer (patient) solutions. Through workshops, interviews, and literature reviews, we gather and synthesize key findings that are aimed to guide those who are planning to use AI.
DNV is involved in two Horizon 2020 research projects and a NFR funded PhD all aimed at using artificial intelligence to improve health outcomes.
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 possible privacy implications.