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A NeuroSymbolic Human-in-the-Loop Approach Towards Fusing Medical Expert Knowledge with ANNs

Authors: Theodoropoulos, Spyros; Makridis, Georgios; Pnevmatikakis, Aristodemos; Moulos, Vrettos; Kyriazis, Dimosthenis; Tsanakas, Panayiotis;

A NeuroSymbolic Human-in-the-Loop Approach Towards Fusing Medical Expert Knowledge with ANNs

Abstract

At the 21st AIAI 2025 conference, a paper was presented that introduced a novel NeuroSymbolic Human-in-the-loop approach for integrating medical expert knowledge with Artificial Neural Networks (ANNs) in healthcare. This innovative framework combines biometric data from wearable devices with AI models, enabling a collaborative process where both AI and healthcare professionals contribute to model training. The system was evaluated on real-world healthcare datasets, particularly focusing on diabetic patients, and aims to enhance chronic disease management through personalized, AI-driven healthcare solutions. The paper was part of the 1st Workshop on SilverTech, which explored the potential of AI technologies, including wearable devices and IoT, in improving healthcare for aging populations. While the workshop focused on the integration of these technologies for personalized healthcare, the paper itself highlighted the use of NeuroSymbolic AI for better healthcare prediction, prevention, and intervention, particularly in chronic disease management.

Keywords

Healthcare AI, AI and healthcare professionals, NeuroSymbolic AI, Chronic disease management, Diabetes prediction, Ambient Assisted Living (AAL), Artificial Neural Networks (ANNs), Wearable devices, SilverTech, Big Data in healthcare, IoT in healthcare

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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