Evidence map›Paper›PMID 42286048›Full record

ArticleScientific reports2026

Artificial intelligence-powered cloud security strategies for protecting critical clinical operations in healthcare environments.

Rinku Sharma Dixit, Shailee Lohmor Choudhary, Nancy Arya, Neeta Nathani, Chaitali Bhowmik, Vandana Roy, Shitanshu Jain

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Rinku Sharma DixitDepartment of Artificial Intelligence and Machine Learning, New Delhi Institute of Management, New Delhi, India.
Shailee Lohmor ChoudharyDepartment of Artificial Intelligence and Machine Learning, New Delhi Institute of Management, New Delhi, India.
Nancy AryaDepartment of Computer Science & Engineering, School of Engineering & Sciences, GD Goenka University, Sohna, Gurugram, Haryana, India.
Neeta NathaniDepartment of Electronics and Communication Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur, MP, India.
Chaitali BhowmikDepartment of Artificial Intelligence and Machine Learning, Vivekananda Institute of Professional Studies-Technical Campus, Pitampura, New Delhi, India.
Vandana RoyDepartment of Electronics and Communication Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur, MP, India. vandana.roy20@gmail.com.
Shitanshu JainDepartment of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India. shitanshu.jain@jaipur.manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing use of cloud computing in hospitals, telemedicine, the Internet of Medical Things (IoMT) and real-time patient monitoring has made for an increasing trend of artificial intelligence-driven cloud security in hospitals. The growing reliance on distributed healthcare clouds layers the cyber-attack surface, however, with critical clinical operations now at risk from ransomware attacks, insider threats, API exploitation, and advanced persistent attacks. This research study introduces a novel AI-integrated cloud security framework tailored for safeguarding mission critical applications in the healthcare sector featuring an intelligent threat detection component, a probabilistic risk evaluation system, and an adaptive response orchestration system. The proposed architecture is built-in by using telemetry normalization, probabilistic behaviour modelling, deep autoencoders for anomaly detection, Bayesian approach for threat probability estimation, multi-objective risk scoring and reinforcement learning for adaptive mitigation. An experimental validation was performed with the CICIDS2017 dataset including around two million samples of network traffic data across various attack categories. The experimental results show that excellent performances have been achieved with an accuracy of 0.96, precision of 0.95, recall of 0.94, F1score of 0.95 and AUC of 0.98 with low latency of around 26 ms and reduced false positive rate of 0.03. A comparative analysis against the current cloud security methods also confirms the effectiveness of the proposed framework in delivering better operational security, response time and the availability of clinical services, providing the continuous clinical service that healthcare organizations require. The research showcases how incorporating AI with responsive cloud security mechanisms can offer a scalable and resilient defense against today's healthcare cloud infrastructures.

Indexed as

Artificial IntelligenceCloud ComputingComputer SecurityDelivery of Health CareAutoencoderBayes TheoremHumansInternet of ThingsArtificial intelligenceBehavioral access controlClinical operations protectionCloud securityFederated threat intelligenceHealthcare cybersecurityIntelligent threat detection

Identifiers

PMID42286048
PMCPMC13507203

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.