Explainable AI for healthcare 5.0: opportunities and challenges

D Saraswat, P Bhattacharya, A Verma, VK Prasad… - IEEe …, 2022 - ieeexplore.ieee.org
In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It
expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital …

A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches

EE Kosasih, E Papadakis, G Baryannis… - International Journal of …, 2024 - Taylor & Francis
Artificial Intelligence (AI) has emerged as a complementary technology in supply chain
research. However, the majority of AI approaches explored in this context afford little to no …

[HTML][HTML] A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection

A Abdulla, G Baryannis - Supply Chain Analytics, 2024 - Elsevier
Supplier selection has become increasingly complex regarding selection criteria caused by
expanded data collection processes and supplier numbers due to globalisation effects. This …

Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0

RC Poonia, HA Al-Alshaikh - Computers in Biology and Medicine, 2024 - Elsevier
Smart healthcare has advanced the medical industry with the integration of data-driven
approaches. Artificial intelligence and machine learning provided remarkable progress, but …

[HTML][HTML] Recent applications of Explainable AI (XAI): A systematic literature review

M Saarela, V Podgorelec - Applied Sciences, 2024 - mdpi.com
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

[PDF][PDF] Advances in machine learning and explainable artificial intelligence for depression prediction

H Byeon - International Journal of Advanced Computer Science …, 2023 - researchgate.net
There is a growing interest in applying AI technology in the field of mental health, particularly
as an alternative to complement the limitations of human analysis, judgment, and …

[PDF][PDF] USING MCDM METHODS TO OPTIMISE MACHINE LEARNING DECISIONS FOR SUPPLY CHAIN DELAY PREDICTION: A STAKEHOLDER-CENTRIC …

M Wyrembek, G Baryannis - Logforum, 2024 - researchgate.net
Background: This study addresses challenges faced by supply chain stakeholders who lack
expert knowledge in making decisions related to Machine Learning. It introduces a novel …

[책][B] Impact of Artificial Intelligence in business and society: Opportunities and challenges

D La Torre, FP Appio, H Masri, F Lazzeri, F Schiavone - 2023 - api.taylorfrancis.com
Belonging to the realm of intelligent technologies, it is increasingly accepted that AI has
evolved from being merely a development standpoint in computer science. Indeed, recent …

Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions

A Brintrup, G Baryannis, A Tiwari, S Ratchev… - arxiv preprint arxiv …, 2023 - arxiv.org
While the increased use of AI in the manufacturing sector has been widely noted, there is
little understanding on the risks that it may raise in a manufacturing organisation. Although …

Explainable artificial intelligence for mental disorder screening: A computational design science approach

S Tutun, K Topuz, A Tosyali… - Journal of Management …, 2024 - Taylor & Francis
Mental disorders affect nearly one billion people globally, 94% of whom are undiagnosed
and untreated due to an acute shortage of trained clinicians. In response to this crisis, this …