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Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

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  • Engr. Rajib Mazumder,
  • Muhammad Anwar Hossain,
  • Dr. Aparna Chakraborty
Engr. Rajib Mazumder
IT Consultant, SDMGA Project, ICT Division, Dhaka, Bangladesh
Muhammad Anwar Hossain
Senior Maintenance Engineer, ICT Division, Dhaka, Bangladesh.
Dr. Aparna Chakraborty
Registrar of Medicine, M Abdur Rahim Medical College, and Hospital, Dinajpur, Bangladesh
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 05 (2024)

Published 2024-05-08

Keywords

  • Self-Learning AI

Copyright (c) 2024 Engr. Rajib Mazumder, Muhammad Anwar Hossain, Dr. Aparna Chakraborty

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats.

The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats.

Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape.

The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures.

The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

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