Emergence of artificial generative intelligence and its potential impact on urology.

INTRODUCTION: Artificial generative intelligence (AGI) and large language models (LLMs) have gained significant attention in healthcare and hold enormous promise for transforming every aspect of our life and urology is no exception. MATERIALS AND METHODS: We conducted a comprehensive literature search of electronic databases and included articles discussing AGI and LLMs in healthcare. Additionally, we have incorporated our experiences interacting with the ChatGPT and GPT-4 in different situations with real case reports and case constructs. RESULTS: Our review highlights the potential applications and likely impact of these technologies in urology, for differential diagnosis, prioritizing treatment options, and facilitating research, surgeon, and patient education. At their current developmental stage, we have recognized the need for concurrent validation and continuous human interaction necessary to induce inverse reinforced learning with human feedback to mature them to authenticity. We need to consciously adjust to the hallucinations and guard patients' confidentiality before their extensive implementations in clinical practice. We propose possible remedies for these shortcomings and emphasize the critical role of human interaction in their evolution. CONCLUSION: The integration of these tools has the potential to revolutionize urology, but it also presents several challenges needing attention. To harness the full potential of these models, urologists must consistently engage in training these tools with their clinical sense and experience. We urge the urology community to actively participate in AGI and LLM development to address potential challenges. These models could help us in unleashing our full potential and help us achieve a better work-life balance.

Use of neural network models to predict liver transplantation waitlist mortality.

Current liver transplantation (LT) organ allocation relies on Model for End-Stage Liver Disease-sodium scores to predict mortality in patients awaiting LT. This study aims to develop neural network (NN) models that more accurately predict LT waitlist mortality. The study evaluates patients listed for LT between February 27, 2002, and June 30, 2021, using the Organ Procurement and Transplantation Network/United Network for Organ Sharing registry. We excluded patients listed with Model for End-Stage Liver Disease (MELD) exception scores and those listed for multiorgan transplant, except for liver-kidney transplant. A subset of data from the waiting list was used to create a mortality prediction model at 90 days after listing with 105,140 patients. A total of 28 variables were selected for model creation. The data were split using random sampling into training, validation, and test data sets in a 60:20:20 ratio. The performance of the model was assessed using area under the receiver operating curve (AUC-ROC) and area under the precision-recall curve (AUC-PR). AUC-ROC for 90-day mortality was 0.936 (95% confidence interval [CI], 0.934-0.937), and AUC-PR was 0.758 (95% CI, 0.754-0.762). The NN 90-day mortality model outperformed MELD-based models for both AUC-ROC and AUC-PR. The 90-day mortality model specifically identified more waitlist deaths with a higher recall (sensitivity) of 0.807 (95% CI, 0.803-0.811) versus 0.413 (95% CI, 0.409-0.418; p < 0.001). The performance metrics were compared by breaking the test data set into multiple patient subsets by ethnicity, gender, region, age, diagnosis group, and year of listing. The NN 90-day mortality model outperformed MELD-based models across all subsets in predicting mortality. In conclusion, organ allocation based on NN modeling has the potential to decrease waitlist mortality and lead to more equitable allocation systems in LT.

Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study.

OBJECTIVE: To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. MATERIALS AND METHODS: The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). RESULTS: The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC-ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR-AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC-ROC of 0.875 (95% CI 0.834, 0.913) and a PR-AUC 0.706 (95% CI, 0.610, 0.790). CONCLUSIONS: The performance of the ML models in the present study was encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.

Artificial intelligence and robotic surgery: current perspective and future directions.

PURPOSE OF REVIEW: This review aims to draw a road-map to the use of artificial intelligence in an era of robotic surgery and highlight the challenges inherent to this process. RECENT FINDINGS: Conventional mechanical robots function by transmitting actions of the surgeon's hands to the surgical target through the tremor-filtered movements of surgical instruments. Similarly, the next iteration of surgical robots conform human-initiated actions to a personalized surgical plan leveraging 3D digital segmentation generated prior to surgery. The advancements in cloud computing, big data analytics, and artificial intelligence have led to increased research and development of intelligent robots in all walks of human life. Inspired by the successful application of deep learning, several surgical companies are joining hands with tech giants to develop intelligent surgical robots. We, hereby, highlight key steps in the handling and analysis of big data to build, define, and deploy deep-learning models for building autonomous robots. SUMMARY: Despite tremendous growth of autonomous robotics, their entry into the operating room remains elusive. It is time that surgeons actively collaborate for the development of the next generation of intelligent robotic surgery.

Evaluation of ChatGPT for Patient Counseling in Kidney Stone Clinic: A Prospective Study.

Introduction: The potential of large language models (LLMs) is to improve the clinical workflow and to make patient care efficient. We prospectively evaluated the performance of the LLM ChatGPT as a patient counseling tool in the urology stone clinic and validated the generated responses with those of urologists. Methods: We collected 61 questions from 12 kidney stone patients and prompted those to ChatGPT and a panel of experienced urologists (Level 1). Subsequently, the blinded responses of urologists and ChatGPT were presented to two expert urologists (Level 2) for comparative evaluation on preset domains: accuracy, relevance, empathy, completeness, and practicality. All responses were rated on a Likert scale of 1 to 10 for psychometric response evaluation. The mean difference in the scores given by the urologists (Level 2) was analyzed and interrater reliability (IRR) for the level of agreement in the responses between the urologists (Level 2) was analyzed by Cohen's kappa. Results: The mean differences in average scores between the responses from ChatGPT and urologists showed significant differences in accuracy (p < 0.001), empathy (p < 0.001), completeness (p < 0.001), and practicality (p < 0.001), except for the relevance domain (p = 0.051), with ChatGPT's responses being rated higher. The IRR analysis revealed significant agreement only in the empathy domain [k = 0.163, (0.059-0.266)]. Conclusion: We believe the introduction of ChatGPT in the clinical workflow could further optimize the information provided to patients in a busy stone clinic. In this preliminary study, ChatGPT supplemented the answers provided by the urologists, adding value to the conversation. However, in its current state, it is still not ready to be a direct source of authentic information for patients. We recommend its use as a source to build a comprehensive Frequently Asked Questions bank as a prelude to developing an LLM Chatbot for patient counseling.