<![CDATA[Incident reporting is a critical component in fostering a culture of patient safety in hospitals. However, the analysis of narrative-based reports is often time-consuming and resource-intensive, thereby hindering the effectiveness of incident reporting and learning systems. Although artificial intelligence (AI) has been widely explored in healthcare, its application in patient safety incident reporting remains limited and under-researched. This study aims to evaluate the use of AI in patient safety incident reporting systems using a literature review method. A total of 179 articles were identified from the ProQuest database through structured searching, and 9 articles were selected for in-depth analysis. The findings indicate that AI, particularly through machine learning and natural language processing (NLP), has been applied to classify incident types, detect risk patterns, and predict events from electronic medical records. Furthermore, AI simplifies the reporting process through free-text narratives, reducing administrative burden and increasing reporter participation. Key challenges in implementation include infrastructure readiness, system integration, and data protection. In conclusion, AI holds significant potential to enhance the efficiency and effectiveness of incident reporting systems, provided it is supported by adaptive and secure implementation strategies.Incident reporting is a critical component in fostering a culture of patient safety in hospitals. However, the analysis of narrative-based reports is often time-consuming and resource-intensive[m1] [AJ2] , thereby hindering the effectiveness of incident reporting and learning systems. Although artificial intelligence (AI) has been widely explored in healthcare, its application in patient safety incident reporting remains limited and under-researched. This study aims to evaluate the use of AI in patient safety incident reporting systems using a literature review method. [m3] [AJ4] A total of 179 articles were identifiedfrom the ProQuest database through structured searching, and 9 articles were selected for in-depth analysis. The findings indicate that AI, particularly through machine learning and natural language processing (NLP), has been applied to classify incident types, detect risk patterns, and predict events from electronic medical records. [m5] [AJ6] Furthermore, AI simplifies the reporting process through free-text narratives, reducing administrative burden and increasing reporter participation. [m7] Key challenges in implementation include infrastructure readiness, system integration, and data protection. In conclusion, AI holds significant potential to enhance the efficiency and effectiveness of incident reporting systems, provided it is supported by adaptive and secure implementation strategies. [m1]Lebih diperjelas dibagian latar belakang masalah [AJ2]Telah disebutkan pada latar belakang paragraf ke-3. [m3]Munculkan kalimat terkait noveltu penelitian ini [AJ4]Telah ditambahkan kalimat yang menunjukkan novelty (eksplorasi AI dalam pelaporan masih terbatas) [m5]Jelaskan metode penelitian ini seperti apa? [AJ6]Sudah ditambahkan menggunakan metode literature review pada kalimat sebelumnya. Pada kalimat ini ditambahkan juga sedikit teknis mengenai database yang digunakan dengan strategi pencarian tertentu. [m7]]]>
