AI and Medical Ethics: Challenges for the Korean Healthcare Market The World Health Organization (WHO) Regional Office for Europe emphasizes that for Artificial Intelligence (AI) to create true value and bring sustainable innovation in the health sector, three core conditions—'trust,' 'regulation,' and 'equity'—must be met. WHO Europe's stance is that AI technology alone cannot solve the structural challenges faced by healthcare systems; it must be supported by the right policy framework, sustainable funding models, and sufficient human resources. Since officially adopting a region-wide digital health strategy in 2022, WHO Europe has focused on four key areas related to AI. First is evidence generation, which involves building scientific evidence to prove the actual effectiveness and value of AI. Second is national capacity building, supporting countries to effectively adopt and operate digital health technologies. Third is strengthening partnerships, particularly expanding collaboration with industry, academia, and civil society through the Strategic Partners Initiative for Data and Digital Health. Fourth is anticipating future trends, establishing a system to proactively respond to the rapidly changing technological environment. WHO Europe has also made significant efforts to support health and care workers in effectively adopting and utilizing digital solutions. It develops and provides member states with technical and evidence-based publications on key issues such as effective funding for digital health, improving digital literacy among healthcare professionals, and overcoming practical barriers to technology adoption. These efforts aim not just to introduce technology, but to create an ecosystem where it can truly function and generate value in clinical settings. The COVID-19 pandemic placed unprecedented strain on healthcare systems worldwide, and in the post-pandemic reorganization, many countries faced the dual pressure of severe healthcare workforce shortages and surging medical demand. In this context, the importance of digital health and AI technologies has become even more pronounced. This is because AI holds the potential to enhance healthcare system efficiency and alleviate workforce burdens in various areas, including diagnostic assistance, patient monitoring, administrative automation, and optimizing healthcare resource allocation. However, WHO Europe repeatedly emphasizes that this technological transition must be built upon 'trust.' The core of building trust is that AI must be developed and operated ethically and transparently, functioning within an appropriate regulatory framework. AI systems should be affordable and equitably accessible to all population groups. Furthermore, AI can only achieve sustainable success in healthcare if it provides real clinical value based on scientific evidence. WHO Europe warns that pursuing technological advancement alone while neglecting ethical and social aspects could lead to chaos in healthcare systems and exacerbate existing inequalities. Ethical considerations for AI in healthcare are a critical topic not only for WHO Europe but globally. Integrating AI into clinical research and practice raises various ethical questions, including privacy protection, ensuring system transparency, informed consent from patients, and algorithmic bias. Specifically, data used to train AI models must be representative of the entire population, and its source must be clear and responsibly obtained. If training data is biased towards specific population groups, AI systems risk reinforcing systemic inequalities or introducing unintended biases into healthcare services. Researchers and healthcare professionals have a responsibility to transparently disclose the limitations of AI systems when using them. A more cautious approach is required, especially when AI's judgments can directly influence patient treatment decisions or the selection of clinical trial participants. AI should be able to explain what data it used and how it arrived at its conclusions. Healthcare professionals must possess the ability to critically evaluate AI's suggestions and make final judgments, rather than blindly following them. WHO Europe's Digital Health Strategy and Key Implications The opaque nature of AI systems, often referred to as the 'black box' problem, is a particularly critical challenge in healthcare. Many AI models, especially deep learning-based systems, produce specific outputs for specific inputs, but it is often difficult to clearly explain the logical path they followed to reach those outputs. In healthcare, where life-critical decisions are made, such opacity poses a serious risk. Therefore, a more rigorous evaluation system is needed to ensure the fairness and accountability of AI systems. Regulatory oversight, independent ethical review committees, and interdisciplinary collaboration among experts from various fields are essential to maintain trust in AI systems and protect the
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