Guiding principles and proposed classification system for the responsible adoption of artificial intelligence in scientific writing in medicine - Hryciw, Brett N.; Seely, Andrew J. E.; Kyeremanteng, Kwadwo (Frontiers in Artificial Intelligence, 2023)
The integration of Large Language Models (LLMs) and Artificial Intelligence (AI) into scientific writing, especially in medical literature, presents both unprecedented opportunities and inherent challenges. This manuscript evaluates the transformative potential of LLMs for synthesis of information, linguistic enhancements, and global knowledge dissemination. At the same time, it raises concerns about unintentional plagiarism, the risk of misinformation, data biases, and an over-reliance on AI. To address these, we propose governing principles for AI adoption that ensure integrity, transparency, validity, and accountability. Additionally, guidelines for reporting AI involvement in manuscript development are delineated, and a classification system to specify the level of AI assistance is introduced. This approach uniquely addresses the challenges of AI in scientific writing, emphasizing transparency in authorship, qualification of AI involvement, and ethical considerations. Concerns regarding access equity, potential biases in AI-generated content, authorship dynamics, and accountability are also explored, emphasizing the human author's continued responsibility. Recommendations are made for fostering collaboration between AI developers, researchers, and journal editors, and for emphasizing the importance of AI's responsible use in academic writing. Regular evaluations of AI's impact on the quality and biases of medical manuscripts are also advocated. As we navigate the expanding realm of AI in scientific discourse, it is crucial to maintain the human element of creativity, ethics, and oversight, ensuring that the integrity of scientific literature remains uncompromised.