Artificial intelligence in urological oncology: An update and future applications

The Current Application and Future Potential of Artificial Intelligence in Renal Cancer

Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and outcome predictions are constantly evolving. We performed a narrative review utilizing PubMed electronic database to query AI as a method of analysis in kidney cancer research. Key search-words included: Artificial Intelligence, Supervised/Unsupervised Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, radiomics, pathomics, and kidney or renal neoplasms or cancer. 72 clinically relevant and impactful studies related to imaging, histopathology, and outcomes were recognized. We anticipate the incorporation of AI tools into future clinical decision-making for kidney cancer.

Performance of ChatGPT-3.5 and ChatGPT-4 on the European Board of Urology (EBU) exams: a comparative analysis

BACKGROUND AND OBJECTIVE

In the transformative era of artificial intelligence, its integration into various spheres, especially healthcare, has been promising. The objective of this study was to analyze the performance of ChatGPT, as open-source Large Language Model (LLM), in its different versions on the recent European Board of Urology (EBU) in-service assessment questions.

DESIGN AND SETTING

We asked multiple choice questions of the official EBU test books to ChatGPT-3.5 and ChatGPT-4 for the following exams: exam 1 (2017-2018), exam 2 (2019-2020) and exam 3 (2021-2022). Exams were passed with ≥60% correct answers.

RESULTS

ChatGPT-4 provided significantly more correct answers in all exams compared to the prior version 3.5 (exam 1: ChatGPT-3.5 64.3% vs. ChatGPT-4 81.6%; exam 2: 64.5% vs. 80.5%; exam 3: 56% vs. 77%, p < 0.001, respectively). Test exam 3 was the only exam ChatGPT-3.5 did not pass. Within the different subtopics, there were no significant differences of provided correct answers by ChatGPT-3.5. Concerning ChatGPT-4, the percentage in test exam 3 was significantly decreased in the subtopics Incontinence (exam 1: 81.6% vs. exam 3: 53.6%; p = 0.026) and Transplantation (exam 1: 77.8% vs. exam 3: 0%; p = 0.020).

CONCLUSION

Our findings indicate that ChatGPT, especially ChatGPT-4, has the general ability to answer complex medical questions and might pass FEBU exams. Nevertheless, there is still the indispensable need for human validation of LLM answers, especially concerning health care issues.

The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review

The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it has shown great promise in the diagnosis of oncologic urological conditions. This paper aims to explore the expanding role of artificial intelligence in the histopathological diagnosis in urological oncology. We conducted a focused review of the literature on AI in urological oncology, searching PubMed and Google Scholar for recent advancements in histopathological diagnosis using AI. Various keyword combinations were used to find relevant sources published before April 2nd, 2024. We approached this article by focusing on the impact of AI on common urological malignancies by incorporating the use of different AI algorithms. We targeted the capabilities of AI's potential in aiding urologists and pathologists in histological cancer diagnosis. Promising results suggest AI can enhance diagnosis and personalized patient care, yet further refinements are needed before widespread hospital adoption. AI is transforming urological oncology by improving histopathological diagnosis and patient care. This review highlights AI's advancements in diagnosing prostate, renal cell, and bladder cancer. It is anticipated that as AI becomes more integrated into clinical practice, it will have a greater influence on diagnosis and improve patient outcomes.

Revolutionary Advances of Robotic Surgery in Urology Field

The advent of robotic surgery has significantly impacted various surgical fields, particularly urology, gynecology, general surgery, and cardiac surgery. While the da Vinci robotic platform has been predominant over the past two decades, recent years have witnessed the emergence of new robotic platforms in Japan, now actively used in clinical practice. Currently, the available systems in Japan, alongside the da Vinci, include the Hinotori, Senhance, Hugo Ras, and Saroa surgical systems. This review focuses on comparing the notable functions of each system in urologic surgery, emphasizing the areas in which they differ from the da Vinci robotic platform. The development of new robotic systems is ongoing, promising not only cost reductions but also the introduction of innovative devices and educational systems. Soft robotics, which constructs robotic devices using soft, adaptable materials, has the potential to become central to the next generation of robotic surgery. Moreover, the collaboration between Artificial Intelligence (AI) and robotic surgery significantly contributes to increasing efficiency, accuracy, and safety in the medical field, with more innovative applications expected in the future.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

A user-friendly deep learning application for accurate lung cancer diagnosis

BACKGROUND

Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis.

METHODS

This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit.

RESULTS

The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful.

CONCLUSIONS

The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses.

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Artificial intelligence for renal cancer: From imaging to histology and beyond

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

Step-by-step optimisation of robotic-assisted radical prostatectomy using augmented reality

Introduction:

Surgical Technique:

Considerations: