인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Background The rise of artificial intelligence in the field of medicine has had a wide-ranging impact, from clinical applications to research. Artificial intelligence-based language models such as Chat Generative Pre-Trained Transformer have the potential to expedite the process of forensic reporting, particularly in forensic medicine. This study aims to evaluate the forensic reporting capabilities of Chat Generative Pre-Trained Transformer-4 in comparison with forensic medicine assistants. The Turkish Penal Code-related forensic medicine guide and 20 case examples were used to train Chat Generative Pre-Trained Transformer-4 in this study. Chat Generative Pre-Trained Transformer-4 was asked to write reports like forensic medicine specialists. In the retrospective phase, 100 forensic cases were assessed by Chat Generative Pre-Trained Transformer-4, while in the prospective phase, 266 new cases were assessed by both Chat Generative Pre-Trained Transformer-4 and forensic medicine assistants. Two forensic medicine specialists assessed the accuracy of these reports in terms of adherence to the forensic medicine guide. Results Chat Generative Pre-Trained Transformer-4 achieved an accuracy rate of 96.6% in the retrospective phase and 96.2% in the prospective phase for the combined categories of “Life-threatening” and “Simple Medical Intervention”. Forensic medicine assistants, however, demonstrated a higher accuracy rate of 99.1% in these categories compared to Chat Generative Pre-Trained Transformer-4. Conclusions The success of Chat Generative Pre-Trained Transformer-4 indicates that the combination of technology and human expertise could establish new standards in forensic reporting. However, it is emphasized that supervision by forensic medicine specialists remains crucial in this process.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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