DigiHealth-AI: Outcomes of the First Blended Intensive Programme (BIP) on AI for Health – a Cross-Disciplinary Multi-Institutional Short Teaching CourseShow others and affiliations
2024 (English)In: JAIR - Journal of Applied Interdisciplinary Research Special Issue (2024): Proceedings of the DigiHealthDay 2023, Deggendorf Institute of Technology , 2024, p. 75-85Conference paper, Published paper (Refereed)
Abstract [en]
We reflect on the experiences in organizing and implementing a high-quality Blended Intensive Programme (BIP) as a joint international event. A BIP is a short programme that combines physical mobility with a virtual part. The 6-day event, titled “DigiHealth-AI: Practice, Research, Ethics, and Regulation”, was organized in collaboration with partners from five European nations and support from the EU’s ERASMUS+ programme in November 2023. We introduced a new learning method called ProCoT, involving large language models (LLMs), for preventing cheating by students in writing. We designed an online survey of key questions, which was conducted at the beginning and the end of the BIP. The highlights of the survey are as follows: By the end of the BIP, 84% of the respondents agreed that the intended learning outcomes (ILOs) were fulfilled, 100% strongly agreed that artificial intelligence (AI) benefits the healthcare sector, 62% disagree that they are concerned about AI potentially eliminating jobs in the healthcare sector (compared to 57% initially), 60% were concerned about their privacy when using AI, and 56% could identify, at least, two known sources of bias in AI systems (compared to only 43% prior to the BIP). A total of 541 votes were cast by 40 students, who were the respondents. The minimum and maximum numbers of students who answered any particular survey question at a given period are 25 and 40, respectively.
Place, publisher, year, edition, pages
Deggendorf Institute of Technology , 2024. p. 75-85
Keywords [en]
Machine learning, healthcare, pedagogy
National Category
Educational Sciences Health Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-110792DOI: 10.25929/dcmwch54OAI: oai:DiVA.org:ltu-110792DiVA, id: diva2:1915646
Conference
DigiHealthDay-2023, International Scientific Symposium, Pfarrkirchen, Germany, Nov 10, 2023
Note
Full text license: CC BY-SA 4.0;
Funder: Knut and Alice Wallenberg Foundations; LTU counterpart fund;
2024-11-252024-11-252024-11-25Bibliographically approved