Evaluation and prioritization of artificial intelligence integrated block chain factors in healthcare supply chain: A hybrid Decision Making Approach

Neda Seifi · Erfan Ghoodjani · Seyed Shabahang Majd · Alireza Maleki · Sayeh Khamoushi

44 citations0/0 files storedComputer and decision making.

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Multi-Criteria Decision Making Blockchain Technology Applications and Security Decision Support System Applications

Abstract

The integration of artificial intelligence and blockchain in healthcare promises a significant transformation in data management, service quality improvement, and increased patient data security. Blockchain, by offering a decentralized and transparent platform, enhances the reliability and security of information. Meanwhile, artificial intelligence, with its ability to analyse and process data, helps identify patterns and predict treatment outcomes. The aim of this study is Evaluation and prioritization of artificial intelligence integrated blockchain factors in the healthcare supply chain using F-AHP and F-DEMATEL. Following a review of previous studies, four criteria and 23 sub-criteria were identified. In the first step, these criteria were ranked using the F-AHP method. In the second step, relationships among the sub-criteria were determined through F-DEMATEL, identifying causal and effect criteria. The F-AHP results show that among the 23 sub-criteria identified from previous studies, "integration of treatment processes (C32)", "Provide fair service (C31)", "health monitoring (C12)", "security of medical data (C34)", and "clinical decision support (C21)" ranked first to fifth, respectively. The F-DEMATEL results indicate that sub-criteria are divided into causal and effect categories, with "stakeholder participation (C42)" and "technology acceptance (C44)" being the most important causal sub-criteria, while "monitoring the treatment process (C15)" and "patient-centered treatment strategies (C22)" were identified as the most important effect sub-criteria. These findings suggest that the use of AI-blockchain integration in healthcare can lead to significant improvements in managing healthcare systems.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 44 citations exist upstream.

References

2019A systematic literature review of healthcare supply chain and implications of future research 2019Base-criterion on multi-criteria decision-making method and its applications 2020Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand 2019Lean supply chain management in healthcare: a systematic review and meta-study 2023Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts 2021A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management 2015Generating a causal model of supply chain collaboration using the fuzzy DEMATEL technique 2021A Deep Learning‐Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.