TY - JOUR
T1 - Viewpoint
T2 - Hybrid Intelligence Supports Application Development for Diabetes Lifestyle Management
AU - Dudzik, Bernd J.W.
AU - van der Waa, Jasper S.
AU - Chen, Pei Yu
AU - Dobbe, Roel
AU - de Troya, Íñigo M.D.R.
AU - Bakker, Roos M.
AU - de Boer, Maaike H.T.
AU - Smit, Quirine T.S.
AU - Dell’Anna, Davide
AU - Erdogan, Emre
AU - Yolum, Pınar
AU - Wang, Shihan
AU - Santamaría, Selene Báez
AU - Krause, Lea
AU - Kamphorst, Bart A.
N1 - Publisher Copyright:
©2024 The Authors.
PY - 2024/7/7
Y1 - 2024/7/7
N2 - Type II diabetes is a complex health condition requiring patients to closely and continuously collaborate with healthcare professionals and other caretakers on lifestyle changes. While intelligent products have tremendous potential to support such Diabetes Lifestyle Management (DLM), existing products are typically conceived from a technology-centered perspective that insufficiently acknowledges the degree to which collaboration and inclusion of stakeholders is required. In this article, we argue that the emergent design philosophy of Hybrid Intelligence (HI) forms a suitable alternative lens for research and development. In particular, we (1) highlight a series of pragmatic challenges for effective AI-based DLM support based on results from an expert focus group, and (2) argue for HI’s potential to address these by outlining relevant research trajectories.
AB - Type II diabetes is a complex health condition requiring patients to closely and continuously collaborate with healthcare professionals and other caretakers on lifestyle changes. While intelligent products have tremendous potential to support such Diabetes Lifestyle Management (DLM), existing products are typically conceived from a technology-centered perspective that insufficiently acknowledges the degree to which collaboration and inclusion of stakeholders is required. In this article, we argue that the emergent design philosophy of Hybrid Intelligence (HI) forms a suitable alternative lens for research and development. In particular, we (1) highlight a series of pragmatic challenges for effective AI-based DLM support based on results from an expert focus group, and (2) argue for HI’s potential to address these by outlining relevant research trajectories.
UR - http://www.scopus.com/inward/record.url?scp=85199567118&partnerID=8YFLogxK
U2 - 10.1613/jair.1.15916
DO - 10.1613/jair.1.15916
M3 - Article
AN - SCOPUS:85199567118
SN - 1076-9757
VL - 80
SP - 919
EP - 929
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -
