LEGAL ANALYSIS OF ARTIFICIAL INTELLIGENCE REGULATION AS A CLINICAL DECISION SUPPORT SYSTEM IN INDONESIAN HOSPITALS: CHALLENGES, ACCOUNTABILITY, AND REGULATORY REFORM
Article Sidebar
Clinical Decision Support System,
Health Law,
Medical Liability,
Hospital Regulation,
Indonesia
Main Article Content
Abstract
Background. The rapid advancement of Artificial Intelligence (AI) has transformed healthcare delivery worldwide, particularly through the development of Clinical Decision Support Systems (CDSS). AI-powered CDSS can assist healthcare professionals in diagnosis, treatment planning, risk prediction, and clinical decision-making. While these technologies offer substantial benefits in improving efficiency and accuracy, they also raise complex legal, ethical, and regulatory questions concerning accountability, patient safety, informed consent, data protection, and medical liability. This study aimed to examine the adequacy of existing Indonesian legal frameworks in regulating AI-based CDSS in hospitals and to identify regulatory gaps that may affect legal certainty and patient protection.
Research Methods. This research employs a normative juridical method. The statutory approach analyzes relevant Indonesian regulations, including: Law No. 17 of 2023 on Health, Law No. 27 of 2022 on Personal Data Protection, Law No. 11 of 2008 on Electronic Information and Transactions, Regulations concerning electronic medical records and hospital governance. The conceptual approach examines legal theories related to professional liability, product liability, patient autonomy, and algorithmic accountability. The comparative approach evaluates regulatory developments from international frameworks.
Findings. Although existing regulations concerning health services, electronic systems, personal data protection, and medical practice provide partial governance, Indonesia has not yet established a comprehensive legal framework specifically addressing AI-assisted clinical decision-making. Consequently, significant uncertainties remain regarding liability allocation, algorithmic transparency, informed consent, and institutional accountability.
Conclusion. The development of a dedicated regulatory framework for AI in healthcare that balances technological innovation with patient safety, ethical governance, and legal certainty.
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