# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 1 Introduction
### Motivation and Contribution  
Artificial intelligence (AI) algorithms are being used in radiology and chronic diseases like cancer to develop precise and effective inventions that will aid in treating people with these conditions and, ideally, finding a cure. AI algorithms have several benefits over conventional analytics and clinical decision-making techniques. As training data are understood by deep learning algorithms, the systems become more accurate. This allows humans to obtain previously unattainable insights into treatment variability, care processes, diagnostics, and patient outcomes. Clinical trials are a crucial component of medical product development because they help to evaluate new treatments' efficacy and uncover any safety concerns. They are also an essential regulatory prerequisite for introducing novel therapies into markets. One of the most difficult and expensive processes in the healthcare sector is conducting clinical studies. Going forward, incorporation of technology would influence clinical development and change the trial procedure. As per the reports of Acumen Research [25], shown in Figure 1, the market for virtual clinical trials was worth USD 10.8 billion in 2021, and it is anticipated to grow to USD 18.6 billion by 2030, at a compound annual growth rate (CAGR) of 6.2% from 2022 to 2030. According to a research study by Accenture, 26% of U.S. consumers have experienced a healthcare data breach and 50% of them became victims of medical identity theft. The average out-of-pocket cost for victims of medical identity theft is estimated at $2500 per incident. The affected patients either changed their healthcare providers or insurance plans or sought legal counsel. Data breaches most frequently occurred in hospitals, urgent care clinics, pharmacies, physicians' offices and health insurance companies [26]. The cost of  
Figure 1: Global virtual clinical trial market overview [25]. a breach in the healthcare industry went up to 42% since 2020. The average total cost of a data breach in the healthcare industry is USD 10.10 million [27]. Recently, this trend is supported by research from Check Point Software Technologies [28], shown in Figure 2, which found that in Q1 2023, the healthcare industry averaged 1684 attacks per week, up by 22% than past year. In 2023, healthcare will be the third most-targeted sector after banking, insurance, and communications. These statistics and facts demonstrate how vulnerable the data assets of people and businesses are. Alarmingly, attackers are specifically targeting the healthcare sector, making it the most open to assault. Medical imaging records are also at danger because of healthcare data breaches, along with patient health details such as insurance member ID and Social Security numbers. A crucial component of patient treatment is medical imaging. The exchange of medical images for the purpose of facilitating care delivery is now possible, thanks to the digitization of imaging data and the frequent storage of these records on picture archiving communication systems (PACS). The broad field of deep learning in medical imaging involves participation from patients, hospitals, research centers, algorithm developers, diagnostic instrument vendors, industry, and legislators. Due to the situation's high level of complexity, the resulting lack of transparency regarding stakeholder motivations and data usage patterns, the ease with which electronic imaging data storage enables data sharing and other factors, the value of individual privacy is threatened, and there is a risk that the restrictions on personal data will be loosened in the name of, at best, scientific advancement and, at worst, financial interests. The field of secure and privacy-preserving deep learning algorithms provides methods to assist in bridging the gap between the protection of personal data and its use for ordinary clinical and research purposes.