CONTENTS
- 1. Medical AI | Concept
- - The Need to Use Medical AI
- 2. Medical AI | Policy and Market Changes Based on Medical Data
- - Global Regulatory Direction
- 3. Medical AI | Key Legal and Ethical Issues
- - Bias and Quality Management Issues in Algorithms Based on Medical Big Data
- - Licensing of Digital Medical Devices and Securing Explainability
- - Lack of Clarity in the Liability Structure Arising from Reliance on AI
- - Protection of Medical Information and Cybersecurity Threats
- - The Need to Address the Gap Among Elderly Users and to Strengthen the Duty to Explain
- 4. Medical AI | Risk Management
- - Advisory and Management Support
1. Medical AI | Concept
Medical AI refers to technology that analyzes vast amounts of medical data to assist decision-making across medical services as a whole, including diagnosis, treatment, research, and operations.
Going beyond merely automating simple, repetitive tasks, it performs advanced functions such as predicting disease patterns, analyzing images, and supporting clinical processes, and it is establishing itself as core infrastructure for medical and research institutions.
In particular, the latest AI technologies, including generative AI, can interpret large-scale clinical data and complex medical patterns with precision, offering new treatment insights and operational efficiencies that were difficult to obtain through conventional methods.
The Need to Use Medical AI
Medical institutions face the challenge of securing both efficiency and patient safety amid structural burdens such as a growing number of patients, staff shortages, rising medical costs, and operational bottlenecks.
AI technology is regarded as a key tool for reducing the burden on medical settings in the following ways.
ㆍ Reducing the likelihood of diagnostic errors and accidents by assisting medical staff in decision-making
ㆍ Enhancing the overall quality of medical services by automating research, administrative, and operational tasks
ㆍ Reducing the time and cost of developing new drugs and medical technologies
For these reasons, AI is spreading as core infrastructure that improves the operational efficiency of medical institutions and enhances the patient experience.
2. Medical AI | Policy and Market Changes Based on Medical Data
The government is pursuing various policies to expand the use of medical data, which is the core foundation of medical AI.
At a recent meeting of the Health and Medical Data Policy Deliberation Committee, discussions were held on strengthening the medical AI ecosystem as a whole, including expanding data infrastructure that the public and private sectors can use together, expanding the demonstration of medical AI, and improving data accessibility.
The main trends are as follows.
▶ Expanding public medical data infrastructure
ㆍ Building national integrated bio big data and opening it in stages
ㆍ Establishing data linkage between institutions and an AI training support system
▶ Improving the usability of medical institution data
ㆍ Expanding medical data use vouchers
ㆍ Establishing standard IRB and DRB procedures and introducing a shared DRB
▶ Advancing the medical AI demonstration environment
ㆍ Supporting the use of AI in regional, essential, and public healthcare
As policy support is strengthened, the adoption of medical AI is expanding beyond simple technology adoption into a complex legal area that encompasses issues of data management, ethics, security, and liability.
Global Regulatory Direction
Internationally, discussions on securing the accountability and safety of medical AI are growing stronger.
The World Health Organization (WHO) has put forward, as the core principles that medical AI must have in order to operate with the public interest at its center, protecting autonomy, promoting safety and well-being, strengthening transparency, securing accountability, ensuring equity, and sustainability, and it proposes these in a way that each country can refer to when establishing policy.
These are becoming a new standard that medical institutions, developers, and regulatory agencies must all observe together, and in international regulatory discussions surrounding medical AI in particular, the following core issues are being addressed as major agenda items.
▷ Data bias
Imbalances in training data may lead to diagnostic errors in certain patient groups.
▷ Lack of algorithmic transparency
When the process by which AI reaches a result is unclear, questions of reliability and explainability may arise.
▷ Personal information protection
Whether pseudonymization and anonymization standards are observed in the course of processing medical data emerges as a legal issue.
▷ Allocation of liability
When the system for allocating liability between medical institutions and developers is unclear in the event of an AI misdiagnosis or error, the risk of disputes increases.
In this way, medical AI is developing into a complex regulatory area that combines technological innovation with ethics, accountability, and governance, and the importance of legal review across approval procedures, data processing systems, and risk management systems is expected to grow further going forward.
3. Medical AI | Key Legal and Ethical Issues
Medical AI technology carries both significant innovation and a high degree of legal sensitivity, so careful legal review and regulatory preparation are needed at every stage of development, adoption, and commercialization.
When medical institutions and technology developers adopt or commercialize AI based diagnostic and treatment technologies, they must review not only technical innovation but also a range of legal and regulatory risks.
In particular, the licensing of digital medical devices, the use of medical big data, the establishment of a personal information protection framework, and liability issues that may arise after an AI solution is commercialized are among the most important review factors in the medical AI field.
Bias and Quality Management Issues in Algorithms Based on Medical Big Data
If the medical big data that forms the foundation of AI training is concentrated in a particular age group, sex, or disease category, the results of the AI's judgment may be distorted.
This affects not only diagnostic accuracy but also medical safety directly.
In addition, if pseudonymization or anonymization is not carried out appropriately, or if data combination procedures fail to meet legal standards, this can lead to a violation of the Personal Information Protection Act, so thorough legal review is required from the data construction stage onward.
Licensing of Digital Medical Devices and Securing Explainability
Diagnostic support and prognostic prediction technologies equipped with AI mostly fall within the category of digital medical devices, so they must satisfy complex regulatory requirements such as licensing procedures, performance verification, and proof of clinical validity.
In particular, if the judgment process of a medical AI is not transparent, questions of explainability may be raised during the approval review, and this can significantly affect the commercialization timeline.
For this reason, it is necessary to design the licensing strategy and the data verification framework together from the early development stage.
Lack of Clarity in the Liability Structure Arising from Reliance on AI
As the commercialization of AI solutions expands, the question of who bears responsibility when incidents occur, such as diagnostic errors, failures in prescription support, or misuse, is emerging as an important issue.
Without clear standards for whether responsibility lies with the medical staff who relied on the AI's judgment, the technology company that developed the algorithm, or the medical institution that provided the data, the risk of disputes grows, so the legal liability structure should be designed at an early stage and related regulations and contracts should be prepared carefully.
Protection of Medical Information and Cybersecurity Threats
Because medical AI processes highly sensitive information, security vulnerabilities can easily emerge during the storage, transmission, and linkage of data.
In particular, externally integrated AI solutions and cloud based platforms carry a high risk of hacking and information leakage, so without an advanced security framework that includes personal information protection, access control, encryption, and log management, this can lead to legal sanctions and a decline in an institution's trustworthiness.
The Need to Address the Gap Among Elderly Users and to Strengthen the Duty to Explain
As the use of AI increases, issues of information access and comprehension may arise for elderly patients, who have a relatively lower ability to use medical services.
Medical institutions have a duty to fully explain the AI based treatment process and the methods of data use to elderly patients, and they should put procedural safeguards in place so that no grounds for a breach of the duty to explain arise.
4. Medical AI | Risk Management
In the medical field, AI technology holds the potential to bring innovative improvements across various areas, including diagnosis, treatment, and operational efficiency.
However, when an AI solution is applied in a clinical setting, complex risks such as technical errors, inadequate data management, and questions of regulatory compliance may arise, and a systematic approach to managing them is needed.
Medical AI requires the licensing of digital medical devices and performance and clinical verification, and legal review from the early stage can optimize the commercialization strategy.
2. Data Protection and Personal Information Issues
To process sensitive medical data safely and comply with legal standards, legal advisory supports the design of data policies.
3. Clarifying Where Responsibility Lies
When an AI error occurs, it is important to clarify the allocation of responsibility between the medical institution and the developer, and to put a proactive response framework in place.
4. Securing Ethics and Governance
Legal advisory plays a key role in securing autonomy, safety, equity, and transparency in line with international recommendations.
5. The Need for a Comprehensive Response Strategy
Because the adoption of medical AI brings both innovation and complex legal risks, professional advice and a systematic response are required.
Advisory and Management Support
Drawing on a deep understanding of the regulatory, legal, and ethical issues surrounding medical AI, our firm's Medical, Bio, and Healthcare Group provides comprehensive advisory across the entire medical AI process.
Our main service areas are as follows.
▷ Collection and use of medical data and personal information protection (pseudonymization and anonymization, MyData)
▷ Support for the development and commercialization of AI based diagnostic and treatment solutions
▷ Advisory on regulatory compliance and liability issues for medical institutions and technology developers
▷ Comprehensive strategic advisory, including the establishment of data use policies, the design of liability structures, and the building of governance
Building on these services, we provide practice oriented legal advice to many medical institutions, research institutions, and medical related companies, and we present strategic solutions that reflect domestic and international regulatory trends and international recommendations.
If you find yourself in a situation that calls for legal advice in connection with medical AI, please feel free at any time to reach out to a 🔗medical attorney for guidance.
