Name in original language
의료 인공지능 연구개발(R&D) 로드맵(안) (’24∼’28)
Initiative overview
The roadmap identifies key structural challenges in the Korean medical AI ecosystem, including a technological gap with leading countries, limited clinical adoption, and barriers related to safety, trust, and system integration. It highlights that although investment and research activity have increased significantly, real-world use remains concentrated in limited areas such as imaging diagnostics, with broader clinical applications still underdeveloped.
To address these issues, the roadmap focuses on expanding AI applications that directly meet unmet needs in clinical settings. It prioritises the development of models for emergency care, major diseases such as cancer, and severe conditions, as well as generative AI systems to support medical workflows. These include tools for communication between doctors and patients, automated medical record generation, and personalised post-treatment care, with an emphasis on improving efficiency and quality of care.
A central pillar is the strengthening of health data utilisation. The roadmap outlines the creation of integrated platforms linking hospital and public-sector data, enabling researchers and companies to access and use datasets more efficiently. It also emphasises the development of large-scale biomedical datasets and the need for interoperability, proposing standardisation efforts and AI-based tools to facilitate data conversion and sharing across institutions.
The strategy also promotes the application of AI across the full lifecycle of medical innovation. This includes expanding AI use in medical devices such as digital therapeutics and surgical robots, and integrating AI into all stages of drug development, from candidate discovery to clinical trials and experimental optimisation. These actions are intended to accelerate research processes and enhance technological competitiveness.
Finally, the roadmap sets out measures to support safe deployment and long-term sustainability. These include clinical validation and real-world testing of AI systems, development of evaluation frameworks to ensure reliability and fairness, and the establishment of ethical guidelines and legal frameworks. It also highlights the need to train specialised professionals through interdisciplinary education programmes and to strengthen governance through coordination mechanisms, stakeholder consultation, and international cooperation.
