Generative Artificial Intelligence in Academic Surgery: Ethical Implications and Transformative Potential

Abstract

Artificial intelligence (AI) is rapidly being used in medicine due to its advanced capabilities in image and video recognition, clinical decision support, surgical education, and administrative task automation. Large language models such as OpenAI’s Generative Pretrained Transformer (GPT)-4 and Google’s Bard have particularly revolutionized text generation, offering substantial benefits for the academic surgeon, including aiding in manuscript and grant writing. However, integrating AI into academic surgery necessitates addressing ethical concerns such as bias, transparency, and intellectual property. This paper provides guidelines and recommendations based on current literature around the opportunities and ethical challenges of AI in academic surgery. We discuss the underlying mechanisms of large language models, their potential biases, and the importance of responsible usage. Furthermore, we explore the ethical implications of AI in clinical documentation, highlighting improved efficiency and necessary privacy concerns. This review also addresses the critical issue of intellectual property dilemmas posed by AI-generated innovations in university settings. Finally, we propose guidelines for the responsible adoption of AI in academic and clinical environments, stressing the need for transparency, ethical training, and robust governance frameworks to ensure AI enhances, rather than undermines, academic integrity and patient care.