Volume 28, Issue 5 (12-2025)
J Arak Uni Med Sci 2025, 28(5): 0-0 |
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Seyednazari M, Gholizad Gougjehyaran H, Sohaili A, Drosti A, Asghari R. The Ethical and Legal Imperative in the Age of AI: Safeguarding Patient Data and Privacy in Healthcare. J Arak Uni Med Sci 2025; 28 (5)
URL: http://jams.arakmu.ac.ir/article-1-8149-en.html
URL: http://jams.arakmu.ac.ir/article-1-8149-en.html
Miramirhossein Seyednazari1 
, Hamed Gholizad Gougjehyaran2 , Amin Sohaili3 , Amirmohammad Drosti4 , Rasul Asghari5
, Hamed Gholizad Gougjehyaran2 , Amin Sohaili3 , Amirmohammad Drosti4 , Rasul Asghari5
1- MSc. in Internal-Surgical Nursing, Department of Internal-Surgical Nursing, School of Medical Sciences, Khoy, Iran
2- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
3- Associate Professor, Department of Nursing, Student Research Committee, Khoy University of Medical Sciences, Khoy, Iran
4- MSc Student, Medical-Surgical Nursing, Student Research Committee, Khoy School of Medical Sciences, Khoy, Iran ,amirdorosti2006@gmail.com
5- BSc in Nursing, Department of Nursing, School of Medical Sciences, Khoy, Iran
2- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
3- Associate Professor, Department of Nursing, Student Research Committee, Khoy University of Medical Sciences, Khoy, Iran
4- MSc Student, Medical-Surgical Nursing, Student Research Committee, Khoy School of Medical Sciences, Khoy, Iran ,
5- BSc in Nursing, Department of Nursing, School of Medical Sciences, Khoy, Iran
Abstract: (381 Views)
The rapid integration of Artificial Intelligence (AI) into medical sciences, while promising transformative breakthroughs in early diagnosis and personalized treatments (1), introduces a profound ethical and legal challenge: the management of the vast, sensitive, and unprecedented volume of health data and the preservation of patient privacy. The nature of this data, which includes clinical records, radiological images, genetic data, and even data from wearable health devices 2, extends beyond traditional identifiable information. It possesses the capability to reconstruct a comprehensive profile of an individual, rendering complete and permanent de-identification virtually impossible (1).
This massive volume of information has become the main fuel for deep learning algorithms, but any breach or disclosure could lead to serious discrimination in access to insurance, employment, and even judicial decision-making (2). The lack of Transparency regarding how these data are processed and analyzed by the algorithms, which often function as a "black box," erodes the trust of both patients and physicians (3). Healthcare providers cannot understand the AI's decision-making process, which not only hinders clinical adoption but also creates a legal gray area concerning accountability in the event of diagnostic or therapeutic error (4).
The current legal challenge stems from the fact that existing privacy laws were not designed to address advanced algorithms and real-time data collection (1, 4). AI constantly outpaces existing legal frameworks by creating novel methods of knowledge extraction from raw data. Furthermore, due to their reliance on large data networks, AI tools are exposed to advanced cyberattacks, which could lead to the mass disclosure of confidential data (5). Consequently, in the absence of a robust and up-to-date data governance framework, AI's potential to improve public health is accompanied by the risk of undermining human dignity and violating fundamental patient rights (6).
To ensure that AI innovations advance with ethical and legal compliance, urgent measures must be taken to establish a comprehensive regulatory framework. This requires formulating a new, dynamic model of informed consent that goes beyond a one-time agreement, allowing patients continuous and informed control over how their data is used at different stages of AI training and deployment. Concurrently, developers must be mandated to embed privacy protection at the core design of every AI tool, which means utilizing advanced privacy-preserving techniques such as differential privacy and federated learning for on-premise data processing. Additionally, a multi-disciplinary oversight body composed of ethics, legal, computer science, and clinical experts must be established, ensuring that every AI tool undergoes a rigorous and transparent ethical and technical assessment and approval process before entering the clinical environment, thereby preventing potential biases and algorithmic errors. These measures will not only protect patients against misuse but also provide the necessary trust for the sustainable and safe advancement of this vital technology in society
This massive volume of information has become the main fuel for deep learning algorithms, but any breach or disclosure could lead to serious discrimination in access to insurance, employment, and even judicial decision-making (2). The lack of Transparency regarding how these data are processed and analyzed by the algorithms, which often function as a "black box," erodes the trust of both patients and physicians (3). Healthcare providers cannot understand the AI's decision-making process, which not only hinders clinical adoption but also creates a legal gray area concerning accountability in the event of diagnostic or therapeutic error (4).
The current legal challenge stems from the fact that existing privacy laws were not designed to address advanced algorithms and real-time data collection (1, 4). AI constantly outpaces existing legal frameworks by creating novel methods of knowledge extraction from raw data. Furthermore, due to their reliance on large data networks, AI tools are exposed to advanced cyberattacks, which could lead to the mass disclosure of confidential data (5). Consequently, in the absence of a robust and up-to-date data governance framework, AI's potential to improve public health is accompanied by the risk of undermining human dignity and violating fundamental patient rights (6).
To ensure that AI innovations advance with ethical and legal compliance, urgent measures must be taken to establish a comprehensive regulatory framework. This requires formulating a new, dynamic model of informed consent that goes beyond a one-time agreement, allowing patients continuous and informed control over how their data is used at different stages of AI training and deployment. Concurrently, developers must be mandated to embed privacy protection at the core design of every AI tool, which means utilizing advanced privacy-preserving techniques such as differential privacy and federated learning for on-premise data processing. Additionally, a multi-disciplinary oversight body composed of ethics, legal, computer science, and clinical experts must be established, ensuring that every AI tool undergoes a rigorous and transparent ethical and technical assessment and approval process before entering the clinical environment, thereby preventing potential biases and algorithmic errors. These measures will not only protect patients against misuse but also provide the necessary trust for the sustainable and safe advancement of this vital technology in society
Keywords: Artificial Intelligence (AI), Medical Ethics, Data Privacy, Health Data, Black Box (Algorithm)
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