Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI.

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.

GridlockED as an Intervention for Nurses (GAAIN) Study.

BACKGROUND: GridlockED (The Game Crafter, LLC) is a serious game that was developed to teach challenges that face nursing and medical professionals in the emergency department (ED). However, few studies have explored nurses' perceptions of the utility, fidelity, acceptability, and applicability of the serious game modality. This study examined how ED nurses view GridlockED as a continuing education platform. METHOD: This single-center observational study explored how nurses engage with and respond to Grid-lockED. The convenience sample included participants recruited from a local continuing nursing education day. Participants completed a presurvey, engaged in a full game play session with the GridlockED game for approximately 45 minutes, and immediately completed a post-game play survey. RESULTS: Of the 48 participants (11 male, 37 female; 44 of 48 were RNs), most (91%) agreed that the workflow reflected in the game was equivalent to the flow in a typical ED. Almost all (96%) found the cases in the game reflective of real ED patients, and most (92%) found the game a useful educational tool to prepare new nurses to transition into the ED environment. CONCLUSION: The GridlockED game shows potential as a serious game to support nursing education, particularly for new ED nurse orientation and transition to ED practice. [J Contin Educ Nurs. 2024;55(5):231-238.].

Existing trends and applications of artificial intelligence in urothelial cancer A scoping review.

INTRODUCTION: The use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC). METHODS: Comprehensive searches based on the concepts of "AI" and "urothelial cancer" were conducted in MEDLINE , EMBASE , Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework. RESULTS: From a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen's kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76. CONCLUSIONS: The use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

Creating GridlockED: A Serious Game for Teaching About Multipatient Environments.

PROBLEM: As patient volumes increase, it is becoming increasingly important to find novel ways to teach junior medical learners about the intricacies of managing multiple patients simultaneously and about working in a resource-limited environment. APPROACH: Serious games (i.e., games not intended purely for fun) are a teaching modality that have been gaining momentum as teaching tools in medical education. From May 2016 to August 2017, the authors designed and tested a serious game, called GridlockED, to provide a focused educational experience for medical trainees to learn about multipatient care and patient flow. The game allows as many as six people to play it at once. Gameplay relies on the players working collaboratively (as simulated members of a medical team) to triage, treat, and disposition "patients" in a manner that simulates true emergency department operations. After researching serious games, the authors developed the game through an iterative design process. Next, the game underwent preliminary peer review by experienced gamers and practicing clinicians, whose feedback the authors used to adjust the game. Attending physicians, nurses, and residents have tested GridlockED for usability, fidelity, acceptability, and applicability. OUTCOMES: On the basis of initial testing, clinicians suggest that this game will be useful and has fidelity for teaching patient-flow concepts. NEXT STEPS: Further play testing will be needed to fully examine learning opportunities for various populations of trainees and for various media. GridlockED may also serve as a model for developing other games to teach about processes in other environments or specialties.