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Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions

arXiv 2206.04793 ↗First posted 30 May 2022cs.CR cs.AIcited by 0

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Abstract (arXiv)

Healthcare systems are increasingly incorporating Artificial Intelligence into their systems, but it is not a solution for all difficulties. AI's extraordinary potential is being held back by challenges such as a lack of medical datasets for training AI models, adversarial attacks, and a lack of trust due to its black box working style. We explored how blockchain technology can improve the reliability and trustworthiness of AI-based healthcare. This paper has conducted a Systematic Literature Review to explore the state-of-the-art research studies conducted in healthcare applications developed with different AI techniques and Blockchain Technology. This systematic literature review proceeds with three different paths as natural language processing-based healthcare systems, computer vision-based healthcare systems and acoustic AI-based healthcare systems. We found that 1) Defence techniques for adversarial attacks on AI are available for specific kind of attacks and even adversarial training is AI based technique which in further prone to different attacks. 2) Blockchain can address security and privacy issues in healthcare fraternity. 3) Medical data verification and user provenance can be enabled with Blockchain. 4) Blockchain can protect distributed learning on heterogeneous medical data. 5) The issues like single point of failure, non-transparency in healthcare systems can be resolved with Blockchain. Nevertheless, it has been identified that research is at the initial stage. As a result, we have synthesized a conceptual framework using Blockchain Technology for AI-based healthcare applications that considers the needs of each NLP, Computer Vision, and Acoustic AI application. A global solution for all sort of adversarial attacks on AI based healthcare. However, this technique has significant limits and challenges that need to be addressed in future studies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 155 references that are in the map. First posted on arXiv 30 May 2022.

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References · 28

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16 more
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