Deep fakes pose a significant threat to medical imaging. These deep fakes appear very similar to real diagnostic scans and are often difficult to distinguish from real medical images. This paper discusses how deepfakes are created and highlights their potential for research and education, as well as risks such as misdiagnosis and data manipulation. We also review various deepfake detection techniques, ranging from traditional image forensics to advanced deep learning models, and highlight the strengths and weaknesses of these approaches for detecting sophisticated deepfakes. We also discuss the ethical issues of deepfakes in healthcare, such as patient privacy, data security, informed consent, algorithmic bias, and the potential loss of trust in medical systems. In addition, we present an experimental study that evaluates how well different deep learning models detect deepfakes in a lung CT scan dataset, demonstrating both the potential and limitations of current detection methods. Finally, we outline future research directions, including real-time detection, explicable AI, enhanced cybersecurity, and strengthened ethical guidelines. This review is a valuable resource for researchers, clinicians, and policymakers interested in exploring AI medical imaging and ethics in the age of deepfakes.

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