ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence.

PURPOSE OF REVIEW: ChatGPT has emerged as a potentially useful tool for healthcare. Its role in urology is in its infancy and has much potential for research, clinical practice and for patient assistance. With this narrative review, we want to draw a picture of what is known about ChatGPT's integration in urology, alongside future promises and challenges. RECENT FINDINGS: The use of ChatGPT can ease the administrative work, helping urologists with note-taking and clinical documentation such as discharge summaries and clinical notes. It can improve patient engagement through increasing awareness and facilitating communication, as it has especially been investigated for uro-oncological diseases. Its ability to understand human emotions makes ChatGPT an empathic and thoughtful interactive tool or source for urological patients and their relatives. Currently, its role in clinical diagnosis and treatment decisions is uncertain, as concerns have been raised about misinterpretation, hallucination and out-of-date information. Moreover, a mandatory regulatory process for ChatGPT in urology is yet to be established. SUMMARY: ChatGPT has the potential to contribute to precision medicine and tailored practice by its quick, structured responses. However, this will depend on how well information can be obtained by seeking appropriate responses and asking the pertinent questions. The key lies in being able to validate the responses, regulating the information shared and avoiding misuse of the same to protect the data and patient privacy. Its successful integration into mainstream urology needs educational bodies to provide guidelines or best practice recommendations for the same.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

URS for de-novo urolithiasis after kidney transplantation: a systematic review of the literature.

INTRODUCTION: There is a gap in the available literature and guidelines concerning the optimal approach for treating allograft stones, which currently include external shockwave lithotripsy, ureteroscopy and laser lithotripsy, or percutaneous nephrolithotomy. The objective of this systematic review was to evaluate the safety and effectiveness of URS as a treatment option for patients in this scenario. EVIDENCE ACQUISITION: A comprehensive search of the literature was conducted until August 2023. Only original articles written in English were considered for inclusion. This review has been registered in PROSPERO (registration number CRD42023451154). EVIDENCE SYNTHESIS: Eleven articles were included (122 patients). The mean age was 46.9±9.5 years, with a male-to-female ratio of 62:49. The preferred ureteral reimplantation technique was the Lich-Gregoire. The mean onset time was 48.24 months. Acute kidney injury, urinary tract infections and fever were the most frequent clinical presentations (18.3% each), followed by hematuria (10%). The mean stone size measured 9.84 mm (±2.42 mm). Flexible URS was preferred over semirigid URS. The stone-free rate stood at 83.35%, while the overall complication rate was 13.93%, with six (4.9%) major complications reported. Stones were mainly composed of calcium oxalate (42.6%) or uric acid (14.8%). Over an average follow-up period of 30.2 months, the recurrence rate was 2.46%. No significant changes in renal function or allograft loss were reported. CONCLUSIONS: URS remains an efficient choice for addressing de-novo allograft urolithiasis, offering the advantage of treating urinary stones with a good SFR and a low incidence of complications. Procedures should be performed in an Endourology referral center.

A content analysis of 'Water Apps' and prevention of urological diseases: Do apps really help?

INTRODUCTION: Maintaining hydration reduces incidence of kidney stone disease (KSD), chronic kidney disease (CKD) and urinary tract infections (UTIs). Mobile applications (apps) measuring hydration are gaining in usage, allowing users to monitor intake whilst also taking into account the signs and symptoms of dehydration. Our study looked at the water apps in the management and/or prevention of urological disease. MATERIAL AND METHODS: The original android app store (Google Play Store), and the Apple App Store (iOS App Store) were searched using the term 'hydration', 'fluid' and 'water'. All apps from each distribution platform, with a minimum of 100 reviews, were then selected and analysed. RESULTS: After identification of 51 applications (13 from Apple App Store, and 38 from Google Play Store), 45 were free to download and 6 were paid (cost range: $2.19-$7.97). While none of the apps facilitated measurement of urine output and colour, 12 mentioned signs and symptoms of dehydration. Furthermore, when calculating required fluid intake, the level of activity was considered by 31 apps. With regards to information provision, only one of the apps included advice or education about urological conditions associated with poor hydration. None of the apps gave advice on hydration related to CKD and UTI. CONCLUSIONS: Mobile phone apps are a well-established tool for measuring fluid intake. However, they provide little information regarding the importance of hydration, and don't utilise other measures such as level of activity, urine output or colour. Considering the increasing popularity of fitness and hydration apps in our daily lives, the developers need to make them more comprehensive and informative.