Saleem Ameen

You are already at the latest version The recognition of surgical instruments using Artificial Intelligence (AI) in Minimally Invasive Surgery (MIS) offers significant opportunities for data-driven improvements in surgical training and patient safety, with surgical instrument recognition being a critical component. MIS remains challenging due to complex intraoperative conditions that limit conventional real-time object detection AI algorithms.

Fig 2. Relational network prepared by the authoring team. Our team’s relational network is visually represented as a series of spatial arrangements, communicating the overall team structure. Additionally, team members are interconnected with color-coded bands that denote functional clusters within the group. Figure representing relationships among team members found at: https://www.teamcard.io/team/demo.

Abstract Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.