Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Messinis, Sotirios; Temenos, Nikos; Protonotarios, Nicholas E.; Rallis, Ioannis; Kalogeras, Dimitrios; Doulamis, Nikolaos

doi:10.1016/j.compbiomed.2024.108036

Cited by 3 publications

(1 citation statement)

References 129 publications

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“…A combination of human knowledge and computer derived assistance could be used to accomplish this. Various fields have already proven that ML is a fast and precise means of obtaining valid results; therefore, physicians can benefit from employing this intelligent technology to make better decisions [4,5]. Doctors and hospitals can regulate this immense amount of data by applying ML techniques, keeping electronic health records of patients accessible from anywhere at any time, and thus being able to provide better treatment.…”

Section: Introductionmentioning

confidence: 99%

The Role of Machine Learning in Managing and Organizing Healthcare Records

Alghamdi,

Al-Khasawneh,

Alarood

et al. 2024

Eng. Technol. Appl. Sci. Res.

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With the exponential growth of medical data, Machine Learning (ML) algorithms are becoming increasingly important to the management and organization of healthcare information. This study aims to explore the role that ML can play in optimizing the management and organization of healthcare records, by identifying the challenges, advantages, and limitations associated with this technology. Consequently, the current study will contribute to the understanding of how ML might be applied to the healthcare industry in a variety of circumstances. Using the findings of this study, healthcare professionals, researchers, and policymakers will be able to make informed decisions regarding the adoption and implementation of ML techniques for regulating healthcare records. The findings of this paper revealed that ML can play an important role in efficiently directing and classifying healthcare records using different perspectives.

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Section: Introductionmentioning

confidence: 99%

The Role of Machine Learning in Managing and Organizing Healthcare Records

Alghamdi,

Al-Khasawneh,

Alarood

et al. 2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

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Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Kumar,

Bernard,

Ullah

et al. 2024

Computers in Biology and Medicine

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Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

Guerra

2024

Ital J Med

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In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.

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Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Cited by 3 publications

References 129 publications

The Role of Machine Learning in Managing and Organizing Healthcare Records

The Role of Machine Learning in Managing and Organizing Healthcare Records

Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

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