Advancing Patient Privacy in the Era of Artificial Intelligence: A Deep Learning Approach to Ensuring Compliance with HIPAA and Addressing Ethical Challenges in Healthcare Data Security

Main Article Content

Kiran Kumar Maguluri
Venkata Krishna Azith Teja Ganti
Tulasi Naga Subhash Polineni
Nareddy abhireddy

Abstract

Great strides have been made in advancing patient care—particularly in the areas of diagnosis, care restoration, and personal health tracking—through the implementation of artificial intelligence tools in healthcare. However, the healthcare industry has only recently begun to address the ethical and legal issues associated with securing patient privacy. While compliance guidelines for the secondary use of protected health information exist, many are overwhelmed by the complexity and lack of understanding associated with ensuring that healthcare data security semantics are in alignment with regulations that enforce these guidelines. Data breaches due to loss or theft of healthcare data can put patients at risk of identity theft and harm.


In addition to meeting regulatory guidelines, healthcare organizations have to navigate the blurred line between two ethical issues that arise as companies try to enhance safety and explore new frontiers in care management. One of these issues primarily looks at patient privacy and what needs to be done to ensure that patients feel their most private information is secure. In contrast, the primary concern that companies are struggling to confront involves dealing with advanced threats that are currently unknown. This ethical concern looks to perpetuate the advancement of patient privacy by leveraging technology and insights that can manage this new, unknown threat space. This text explores patient concerns about privacy and the regulatory push to secure sensitive data. It provides insight into issues regarding patient privacy and the shared empathy around this topic. It also investigates the mismatch between the law and technology as it is currently being implemented and discusses next steps for how deep learning can be utilized to show compliance with existing law and expanded for additional ethical considerations. It closes with a discussion on disclosure, mitigating concerns, and possible future work. This work aims to validate concerns from all parties about healthcare data security and privacy. Moreover, the necessity for incorporation of advanced technology, such as deep learning, in an expeditious manner has become compounded due to the recent explosion in digital health applications and predictive models that are both driving AI and potentially missing the intent of recent regulatory efforts. Essentially, regulation has to catch up with science.


 

Article Details

How to Cite
Kiran Kumar Maguluri, Venkata Krishna Azith Teja Ganti, Tulasi Naga Subhash Polineni, & Nareddy abhireddy. (2024). Advancing Patient Privacy in the Era of Artificial Intelligence: A Deep Learning Approach to Ensuring Compliance with HIPAA and Addressing Ethical Challenges in Healthcare Data Security. International Journal of Medical Toxicology and Legal Medicine, 27(5), 217–228. https://doi.org/10.47059/ijmtlm/V27I5/030
Section
Articles