## 5 AI Regulations, Verification, and Ethical Problems
### Regulations on AI in Medicine  
In the European Union [119], diagnostic systems based on AI are classified as \"high risk\". This means that they must undergo detailed evaluation procedures and have specific documentation and test results confirming their effectiveness and safety before implementation.  
In the US, AI used in diagnostics is regulated by the FDA [120] under the 510(k) procedure. This means that manufacturers of such systems must demonstrate how they update their models and verify their safety and effectiveness (as part of the \"Software as a Medical Device\" approach). Regular algorithm updates must meet FDA requirements, which affects the pace of corrections and improvements to diagnostic systems.  
Although ethical and regulatory issues related to AI in dermatology are widely discussed in the literature, practical problems resulting from its use still require specific examples. Cases related to breaches of patient confidentiality are significant. In the UK, in 2021, a case involving a teledermatology app that shared patient imaging data without sufficient user consent gained high-profile attention. It led to an investigation by the British data protection authority, the Information Commissioner's Office (ICO), and resulted in its suspension for several months, significantly limiting patients' access to remote dermatological consultations [121].  
In Germany, on the other hand, the implementation of the EU Regulation on Artificial Intelligence (AI Act) requirements necessitated the thorough verification of diagnostic models based on deep learning. As a result, some AI solutions used in dermatological diagnostics were temporarily withdrawn from clinics, pending detailed audits confirming their safety and compliance with European regulations [122].  
Additionally, it is worth noting the significant ethical issues related to the use of AI in dermatological diagnostics in the United States, where the FDA has imposed specific requirements for the transparency of diagnostic algorithms. For example, an app using AI to assess patients' risks of melanoma was subject to additional scrutiny because it did not meet the requirements for clear explanations of the basis for the system's decisions. This led to delays in the implementation of the app in clinics and reduced trust in the technology by both physicians and patients [123].  
An example of regulatory challenges outside Europe and the US can be found in India, where the lack of adequately formulated regulations on AI and patient data protection has resulted in many dermatological AI apps currently operating without clear standards for data protection or diagnostic transparency. This has led to growing concerns about the quality of diagnoses and the potential for abuse by commercial providers of these technologies [124].  
In January 2024, the FDA made a landmark decision to approve the first AI skin cancer detection device (DermaSensor) for primary care use [125]. It represents a new regulatory precedent--the first time that an AI dermatology device has been approved for use by non-specialists. The authors state that this approval validates AI's potential to improve access to care but also raises questions about the need for ongoing evidence gathering and regulatory adaptation to rapidly evolving AI technologies.  
All these examples demonstrate the importance of considering ethical and regulatory aspects in detail and in context when implementing AI solutions in dermatology in order to avoid not only formal legal issues but also harm to patients and a decline in public trust in modern technologies.