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Background: Nowadays, the question of how the moral aspects of health can be put into the framework of patient-physician relationship has turned into a current debate in the area of medicine and patients’ rights. This study was conducted to examine the effect of virtual education through sending emails to physicians on their application of Islamic and Quranic codes of ethics. Materials and Methods: In this interventional study, 75 general practitioners and specialists in Kermanshah were selected through non-randomized simple sampling method. The means of data collection were physicians’ ethical conduct checklist and a questionnaire which was prepared by the researcher according to the ethical traits extracted from Quran and Hadith. Concepts extracted from Islamic resources were sent to the physicians email addresses 3 times a week. Data were analyzed through dependent t-test using SPSS software version15. Results: The major findings of the study indicated the positive effects of online Quran teaching on enhancing the application of the ethical codes of conduct and stabilizing the professional codes of ethics. Conclusion: Although improving professional ethical conduct requires long-term training programs, the findings of this study indicated that religious education can be effective in using the general codes of ethics.

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Background and Aim The occupational environment, the type of work overload or underload, physical risks, how individuals adapt to the workplace, and face the family - work constitute the sources of stress or occupational distress. Job stress emerges as the duties and tasks assigned to people are more than their abilities. This study aimed to investigate the relationship between stress management training and work ethics of employees.
Methods & Materials This research was an applied study in terms of purpose, a mixed exploratory (qualitative and quantitative) study in terms of data, a content analysis (qualitative stage) and  cross-sectional survey (quantitative stage) in term of conduct. The study population in the qualitative section comprised experts (Experts in Psychology, Educational Management, and Social Medicine) according to the inclusion and exclusion criteria. In the qualitative section, the study population included experts and managers who had received stress management training. Sample size and sampling method in qualitative part was based on the principle of theoretical saturation, resulting in 12 people using purposive sampling method. In the quantitative part, the sampling was based on the Cochran’s formula, resulting in 220 people who were selected using cluster sampling method. 
Ethical Considerations This barcode research was presented to the Ethics Committee and registered in the system (IR.IAU.TNB.REC.1400.121). 
Results The results showed that to deal with stressful situations, three basic strategies of event-focused coping, anxiety-focused coping, and avoidance-focused coping strategies can be used for stress management training models. Overall, the components presented for coping with stressful conditions have the necessary and appropriate validities. All three main coping strategies (event-focused, anxiety-focused, and refusal-focused) are suitable for explaining and fitting. So, they all are reliable and confirmed in the current research questionnaire of coping with stress.
 Conclusion Strategies for coping with stressful situations have a different effect on people’s professional work ethics so that the event-focused coping strategy has a significant positive relationship with professional ethics. On the other hand, the anxiety-focused and avoidance-focused coping strategies have negative and decreasing relationship with professional ethics. The more emphasis on the event-focused coping strategy, the more would be the professional ethics of individuals and the more emphasis on the anxiety-focused and avoidance-focused coping strategies, the less would be the professional ethics of individuals..

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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