Our first publication is here – AI in Healthcare
Artificial intelligence (AI) is significantly changing the healthcare field, from data analytics to policy and from patient empowerment to behavior support systems. In May, the first WASP-HS Community Reference Meeting (CRM) was hold about AI in Healthcare.
AI-driven technologies are changing the way clinical providers make decisions and how patients find and engage with their own health.
– AI has already been successfully applied to improve the speed and accuracy in the use of diagnostics, give practitioners faster and easier access to more knowledge, and enable remote monitoring and patient empowerment through self-care, says Virginia Dignum, Program Officer at WASP-HS.
WASP-HS CRM meetings aim to help public and private organizations in Sweden with challenges and questions regarding their interests, as well as developments within WASP-HS. Participants in the meeting included researchers from most Swedish universities, industry, and national and regional governments, as well as general public and international organizations.
Future Road Map
The discussions highlighted the importance of social and democratic principles – at the core of Swedish society and political tradition – for the development and use of AI in healthcare and medicine, including responsibility, participation, inclusion and diversity, grounded on a fundamental respect for human agency and self-determination.
A research road map that ensures alignment with these principles should include efforts in:
- Infrastructures, including structured information models in our healthcare systems, and laws that enable. 1) multidisciplinary research providing new evidence-based knowledge based on shared data across organizations, and 2) co-creation of new AI systems across academia, healthcare organizations, industry, public authorities, citizens and NGOs.
- Methodology and instruments that allow citizens and other stakeholders to engage in an AI system design process
- Multidisciplinary research on individuals’ identity in an AI future, their relationship to their data and health trajectories, to healthcare and society, to AI systems, privacy, and legislation about the use of AI systems.
- Data governance, including theory and practice on data aggregation, translation and harmonization, considering the role and aims of different actors.
- A sound legal and ethical scaffolding framework to ensure trust and deal with issues of liability and responsibility.
- The need and instruments to question and contest the idea that prediction is only possible if enough data is available.