Assessing the Impact of Medical Ethics Violations on the Effectiveness of Artificial Intelligence in Telemedicine

In recent years, telemedicine has become very important in the world of healthcare. In particular, the use of telemedicine has increased during the Covid-19 global pandemic. Through telemedicine, patients get remote services through information technology. Along with the development of AI today, AI has also been implemented in telemedicine and has provided many benefits to the health industry. However, there have been cases in the implementation of AI that have caused violations of medical ethics that have raised concerns, including AI issuing prescriptions without supervision, which can result in organ damage and even death. This study evaluates the impact of medical ethics violations on the effectiveness of AI in telemedicine. Systematic Literature Review (SLR) is used as the research method. The keywords used in this study are “telemedicine”, “artificial intelligence”, “medical ethics” from Scopus and Pubmed databases. A variety of studies were included in the review, including clinical trials, case studies, and theoretical frameworks. The review identified several trends, including the growing reliance on AI for diagnosis and treatment recommendations. In addition, recurring concerns were identified regarding the potential for bias and the security of the data. The findings indicate that violations of medical ethics can significantly impede the efficacy of AI in telemedicine, particularly with regard to ethical principles such as beneficence, nonmaleficence, autonomy, and justice. Such violations include infringements upon the right of patients to refuse AI-driven treatment, the transmission of unsecured data, the occurrence of data breaches, erroneous diagnoses, and the presence of bias in AI-generated recommendations. These issues have a deleterious impact upon patient safety and trust, result in a reduction of the accuracy of AI-facilitated diagnoses, and perpetuate health inequalities, ultimately eroding public trust and acceptance of AI in healthcare.