A New Era of Treatment Opened by Advanced Medical Technology AI-based medical innovations, once seen only in movies, are now becoming a reality. These include optimal drug combinations for cancer treatment, early diagnosis of rare diseases, and personalized treatment plans developed based on individual genomic information. These are no longer technologies of the distant future but scientific achievements already being utilized in some hospitals and research institutes. According to 'AI-Powered Precision Medicine: Hype or Hope?' published by MIT Technology Review on April 4, 2026, AI-driven personalized treatments meticulously analyze patients' genomic information, medical records, and lifestyle data to propose optimal therapies, significantly improving treatment efficacy and patient survival rates. Notably, the report presents specific data-backed examples where AI dramatically enhances the accuracy of drug response prediction. In South Korea, interest in adopting such AI-based precision medicine technologies is also growing. The country possesses a high-level healthcare system and IT infrastructure, creating a favorable environment for the introduction of AI medical technologies. However, behind these hopeful prospects lie complex ethical issues, specifically concerning the limitations of AI technology and healthcare equity. The potential for AI algorithm bias or data misuse to lead to incorrect decisions, privacy infringements, and issues of healthcare accessibility due to the high cost of these treatment technologies are being raised. A column titled 'The Ethical Minefield of AI-Powered Personalized Medicine,' published by The New York Times on April 6, 2026, directly addresses these very problems. One of the biggest advantages of AI-powered personalized treatments is their ability to analyze patterns in medical data that were previously difficult to detect, enabling customized diagnoses and drug therapies. For instance, a case was reported in the United States where AI was used to identify the precise cause of a rare disease in a pediatric patient, leading to successful individualized treatment. MIT Technology Review commented, "AI technology is not merely increasing efficiency; it is fundamentally reshaping the life sciences by solving problems that were previously impossible." The publication particularly highlighted detailed examples in cancer treatment where AI accurately predicted drug responses that were difficult to foresee with traditional methods by analyzing patients' gene mutation patterns. Such technology holds significant potential as a new breakthrough in diagnosing and treating diseases that have remained unresolved by conventional approaches. However, such technology does not offer equal benefits to everyone. Siddhartha Mukherjee, a renowned oncologist, Pulitzer Prize-winning author, and author of The New York Times column, points out that "currently, AI precision medicine risks being exclusively available to the wealthy due to its high cost," emphasizing the need to prioritize healthcare equality. He warns of the danger that AI-based treatment technologies could devolve into 'treatment for the rich,' stressing that these advanced technologies could exacerbate healthcare inequality. Developing AI-based treatment technologies requires immense cost and time, which could lead to the burden of expenses being passed on to patients. Furthermore, without guaranteed transparency in data utilization, there is a possibility that sensitive personal medical information could be commercially exploited. This is an equally important issue that must be addressed in South Korea. Ethical Issues and Equity Facing AI Medicine Medical ethics experts warn that AI algorithms in healthcare could exclude or discriminate against specific groups. Mukherjee's column delves deeply into the issue of algorithmic bias. If the data used to train AI systems is skewed towards particular races, genders, or age groups, the algorithms may produce inaccurate or discriminatory results for other populations. For example, even for patients with the same symptoms, some algorithms might excessively consider or, conversely, overlook differences in race, gender, or age, potentially leading to unfair medical services. Indeed, cases have been reported in the United States where AI for skin cancer diagnosis, primarily trained on data from white patients, failed to accurately diagnose skin cancer in patients of color. This bias fundamentally threatens the reliability of AI medical technology. Internationally, there is a growing call for legal and institutional regulations to address this. While the South Korean government has already enacted some legislation regarding the use and protection of medical data, it is still considered insufficient to keep pace with the rapid advancement of such technologies. Despite the success stories of AI-based medicine, issues of malfunction and accountability also present unresolved ethical dilemmas. If
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