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. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight and task-specific performance reporting. We also introduce an interactive website (https://tripod-llm.vercel.app/) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility and clinical applicability of LLM research in healthcare through comprehensive reporting.

Objectives To determine the extent and content of academic publishers’ and scientific journals’ guidance for authors on the use of generative artificial intelligence (GAI). Design Cross sectional, bibliometric study. Setting Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. Participants Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. Main outcome measures The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. Results Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. Conclusions Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.

Article: Impact of the COVID-19 pandemic on urology residency training in Italy - Minerva Urologica e Nefrologica 2020 August;72(4):505-9 - Minerva Medica - Journals

Utilization of robotic partial nephrectomy has increased significantly. We report a literature wide systematic review and cumulative meta-analysis to critically evaluate the impact of surgical factors on the operative, perioperative, functional, oncologic and survival outcomes in patients undergoing robotic partial nephrectomy. All English language publications on robotic partial nephrectomy comparing various surgical approaches were evaluated. We followed the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) statement and AHRQ (Agency for Healthcare Research and Quality) guidelines to evaluate PubMed®, Scopus® and Web of Science™ databases (January 1, 2000 to October 31, 2016, updated June 2017). Weighted mean difference and odds ratio were used to compare continuous and dichotomous variables, respectively. Sensitivity analyses were performed as needed. To condense the sheer volume of analyses, for the first time data are presented using novel summary forest plots. The study was registered at PROSPERO (https://www.crd.york.ac.uk/prospero/, ID CRD42017062712). Our meta-analysis included 20,282 patients. When open partial nephrectomy was compared to robotic partial nephrectomy, the latter was superior for blood loss (weighted mean difference 85.01, p 0.0001) and postoperative complications (OR 1.27, p = 0.0003), positive margins (OR 2.01, p <0.0001), percentage decrease of latest estimated glomerular filtration rate (weighted mean difference -1.97, p = 0.02) and overall mortality (OR 2.98, p = 0.04). Hilar control techniques, selective and unclamped, are effective alternatives to clamped robotic partial nephrectomy. An important limitation is the overall suboptimal level of evidence of publications in the field of robotic partial nephrectomy. No level I prospective randomized data are available. Oxford level of evidence was level II, III and IV in 5%, 74% and 21% of publications, respectively. No study has indexed functional outcomes against volume of parenchyma preserved. Based on the contemporary literature, our comprehensive meta-analysis indicates that robotic partial nephrectomy delivers mostly superior, and at a minimum equivalent, outcomes compared to open and laparoscopic partial nephrectomy. Robotics has now matured into an excellent approach for performing partial nephrectomy for renal masses.

As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in urology. A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology. The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non-oncological diseases like stones and functional urology. AI technologies are growing their role in health care; but, up to now, their "real-life" implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.