Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Viewpoint: Hybrid Intelligence Supports Application Development for Diabetes Lifestyle Management
Authors: Bernd J. W. Dudzik, Jasper S. van der Waa, Pei-Yu Chen, Roel Dobbe, Íñigo M.D.R. de Troya, Roos M. Bakker, Maaike H. T. de Boer, Quirine T.S. Smit, Davide Dell'Anna, Emre Erdogan, Pinar Yolum, Shihan Wang, Selene Baez Santamaria, Lea Krause, Bart A. Kamphorst
JAIR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | Bernd J. W. Dudzik EMAIL Delft Univeristy of Technology, 2628 XE Delft, The NetherlandsJasper S. van der Waa EMAIL TNO 3769 DE Soesterberg, The NetherlandsPei-Yu Chen EMAIL Roel Dobbe EMAIL I nigo M.D.R.de Troya I.M.D.R.de EMAIL Delft Univeristy of Technology, 2628 XE Delft, The NetherlandsRoos M. Bakker EMAIL TNO 3769 DE Soesterberg, The Netherlands & Leiden University 2311 BE Leiden, The NetherlandsMaaike H. T. de Boer EMAIL Quirine T. S. Smit EMAIL TNO 3769 DE Soesterberg, The NetherlandsDavide Dell Anna EMAIL Emre Erdogan EMAIL Pınar Yolum EMAIL Shihan Wang EMAIL Utrecht University 3584 CS Utrecht, The NetherlandsSelene B aez Santamar ıa EMAIL Lea Krause EMAIL Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The NetherlandsBart A. Kamphorst EMAIL Wageningen University & Research, 6706 KN Wageningen, The Netherlands |
| Pseudocode | No | The paper discusses conceptual challenges and outlines research trajectories for Hybrid Intelligence in Diabetes Lifestyle Management, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a viewpoint article outlining research trajectories and does not describe a specific methodology or implementation for which source code would be provided. There is no mention of code release or repository links. |
| Open Datasets | No | The paper refers to 'results from an expert focus group' and 'three participatory design sessions' for gathering insights. However, it does not provide concrete access information (specific link, DOI, repository name, formal citation) for any publicly available or open dataset used for empirical analysis. |
| Dataset Splits | No | The paper does not describe any empirical experiments using datasets that would require explicit training/test/validation splits. Therefore, no such information is provided. |
| Hardware Specification | No | The paper is conceptual and discusses research trajectories; it does not report on any computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is a conceptual viewpoint outlining research directions. It does not describe any implemented systems or methodologies that would require a list of specific software dependencies with version numbers. |
| Experiment Setup | No | The paper presents a conceptual viewpoint and outlines research trajectories rather than describing specific experiments. Consequently, it does not provide details on experimental setup, hyperparameters, or training configurations. |