The Future of Security in Medical Administration: Ai and Machine Learning in Threat Detection

The future of security in medical administration is increasingly intertwined with advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). As healthcare systems digitize and adopt Electronic Health Records (EHRs), telemedicine, and patient management software, the risk of data breaches and cyberattacks escalates. AI and ML present promising solutions for enhancing security by detecting, preventing, and mitigating threats in real-time. This paper explores the integration of AI and ML in healthcare security, examining their role in improving threat detection, streamlining incident response, and safeguarding sensitive patient data. By leveraging AI and ML algorithms, medical administrators can proactively defend against evolving cybersecurity threats, ensuring the safety of both healthcare providers and patients. The use of AI and ML not only strengthens the security posture of healthcare organizations but also helps maintain trust and compliance with regulatory frameworks such as HIPAA and GDPR.

Authors

Publication Details

Journal
Power System Technology
Published
2024-12-27
DOI
https://doi.org/10.52783/pst.1282
Primary Topic
Information and Cyber Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Future of Security in Medical Administration: Ai and Machine Learning in Threat Detection

Ms.Manal Mazyad Alotaibi
Power System Technology
Information and Cyber Security
article

The Future of Security in Medical Administration: Ai and Machine Learning in Threat Detection

Ms.Manal Mazyad Alotaibi
article en

Abstract

The future of security in medical administration is increasingly intertwined with advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). As healthcare systems digitize and adopt Electronic Health Records (EHRs), telemedicine, and patient management software, the risk of data breaches and cyberattacks escalates. AI and ML present promising solutions for enhancing security by detecting, preventing, and mitigating threats in real-time. This paper explores the integration of AI and ML in healthcare security, examining their role in improving threat detection, streamlining incident response, and safeguarding sensitive patient data. By leveraging AI and ML algorithms, medical administrators can proactively defend against evolving cybersecurity threats, ensuring the safety of both healthcare providers and patients. The use of AI and ML not only strengthens the security posture of healthcare organizations but also helps maintain trust and compliance with regulatory frameworks such as HIPAA and GDPR.

Power System TechnologyVol. 48(4)
Openalex Percentile: Top 55%
Information and Cyber Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.