The increasing usage of artificial intelligence in MRI disease classification and diagnosis presents several ethical impli- cations related to patient privacy, data security, and responsible use. This paper will review some current use cases of AI-based MRI image classification models and propose a framework for ethics policymakers and medical information officers to ensure patient safety and responsible usage of AI in clinical settings.

This project proposes a general guideline that policymakers and clinicians are encouraged to consult before deciding to implement an AI-based software in a clinical setting. By following these guidelines, anyone looking to implement an AI-based software can ensure that it will be used ethically and will protect patient privacy and sensitive information.

The increasing popularity of Artificial Intelligence (AI) has led to its growing usage in the healthcare industry. The most common use cases range from improving patient interactions to helping physicians in their diagnosis. An area of interest for clinicians has been the upcoming use of image- based detection models that assists in disease diagnosis. These machine learning algorithms use patient data to train and test the model and eventually outline areas of concern. Given this new and emerging application, it is increasingly important for healthcare policy makers as well as private hospitals to understand the ethical implications associated with its use. This paper will delve into some current use cases of AI for MRI classification and disease diagnosis. It will also present a framework that policymakers and medical information officers should consult when assessing the ethical validity of an AI- based service. This paper will address the ethical implications of AI usage in MRI technology for diagnostic purposes through three main phases. The initial section of this paper will provide a review of the ongoing use cases of AI in MRI technology, providing a background on the datasets, model accuracy, and overt ethical implications. The second phase of this paper will address these specific ethical issues and will serve as a basis for producing a guideline for policy makers and medical information officers who are looking to implement the model for clinical use. The final phase will assess the validity and necessity of such a guideline.