Artificial intelligence in urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea

Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer

The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.

Assessment of artificial intelligence performance in answering questions on onabotulinum toxin and sacral neuromodulation

PURPOSE

This study aimed to evaluate the performance of three artificial intelligence (AI) models-ChatGPT, Gemini, and Copilot-in addressing clinically relevant questions about onabotulinum toxin and sacral neuromodulation (SNM) for the management of overactive bladder (OAB).

MATERIALS AND METHODS

A set of 30 questions covering mechanisms of action, indications, contraindications, procedural details, efficacy, and safety profiles was posed to each AI model. Responses were assessed by a panel of four urology specialists using predefined criteria: accuracy, completeness, clarity, and consistency. A multi-dimensional scoring framework evaluated the performance across five dimensions: factual accuracy, relevance, clarity/coherence, structure, and utility. Responses were scored on a 4-point Likert scale, and statistical analyses were conducted using one-way ANOVA to compare model performance.

RESULTS

ChatGPT achieved the highest mean score (3.98/4) across all dimensions, with statistically significant differences compared to Gemini (3.20/4) and Copilot (2.60/4) (p=0.001 for all dimensions). ChatGPT excelled particularly in clinical application, procedure, and safety categories, consistently delivering accurate and comprehensive answers. No statistically significant differences were found between Gemini and Copilot in most categories.

CONCLUSIONS

ChatGPT demonstrated superior performance in generating accurate, complete, and clinically relevant responses for OAB management, highlighting its potential as a reliable tool for both healthcare professionals and patients. However, the variability observed in Gemini and Copilot underscores the need for further refinement of these models. Future studies should explore real-world integration of AI models into clinical workflows to enhance patient care and decision-making.

Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper

Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data collected from patients admitted to a high-volume specialized hospital for orthopedic surgery, where COVID-19 is only a secondary diagnosis. This disparity arises despite the two hospitals being geographically close (within20 kilometers). While machine learning can facilitate rapid public health responses, rigorous external validation and continuous monitoring are essential to ensure reliability and safety.

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied. This gap in research can primarily be attributed to the limited availability of extensive datasets including benign tumor and normal tissue in addition to specific type of renal cell carcinoma, which hampers the ability to conduct comprehensive studies in these broader categories. This research introduces a model aimed at classifying renal tissue into three primary categories: normal (non-neoplastic), benign tumor, and malignant tumor. Utilizing digital pathology while slide images (WSIs) from nephrectomy specimens of 2,535 patients from multiple institutions, the model provides a foundational approach for distinguishing these key tissue types. The study utilized a dataset of 12,223 WSIs comprising 1,300 WSIs of normal tissue, 700 WSIs of benign tumors, and 10,223 WSIs of malignant tumors. Employing the ResNet-18 architecture and a Multiple Instance Learning approach, the model demonstrated high accuracy, with F1-scores of 0.934 (CI: 0.933-0.934) for normal tissue, 0.684 (CI: 0.682-0.687) for benign tumors, and 0.878 (CI: 0.877-0.879) for malignant tumors. The overall performance was also notable, achieving a weighted average F1-score of 0.879 (CI: 0.879-0.880) and a weighted average area under the receiver operating characteristic curve of 0.969 (CI: 0.969-0.969). This model significantly aids in the swift and accurate diagnosis of renal tissue, encompassing non-neoplastic normal tissue, benign tumor, and malignant tumor.

Selection of Convolutional Neural Network Model for Bladder Tumor Classification of Cystoscopy Images and Comparison with Humans

Purpose: An investigation of various convolutional neural network (CNN)-based deep learning algorithms was conducted to select the appropriate artificial intelligence (AI) model for calculating the diagnostic performance of bladder tumor classification on cystoscopy images, with the performance of the selected model to be compared against that of medical students and urologists. Methods: A total of 3,731 cystoscopic images that contained 2,191 tumor images were obtained from 543 bladder tumor cases and 219 normal cases were evaluated. A total of 17 CNN models were trained for tumor classification with various hyperparameters. The diagnostic performance of the selected AI model was compared with the results obtained from urologists and medical students by using the receiver operating characteristic (ROC) curve graph and metrics. Results: EfficientNetB0 was selected as the appropriate AI model. In the test results, EfficientNetB0 achieved a balanced accuracy of 81%, sensitivity of 88%, specificity of 74%, and an area under the curve (AUC) of 92%. In contrast, human-derived diagnostic statistics for the test data showed an average balanced accuracy of 75%, sensitivity of 94%, and specificity of 55%. Specifically, urologists had an average balanced accuracy of 91%, sensitivity of 95%, and specificity of 88%, while medical students had an average balanced accuracy of 69%, sensitivity of 94%, and specificity of 44%. Conclusions: Among the various AI models, we suggest that EfficientNetB0 is an appropriate AI classification model for determining the presence of bladder tumors in cystoscopic images. EfficientNetB0 showed the highest performance among several models and showed high accuracy and specificity compared to medical students. This AI technology will be helpful for less experienced urologists or nonurologists in making diagnoses. Image-based deep learning classifies bladder cancer using cystoscopy images and shows promise for generalized applications in biomedical image analysis and clinical decision making.

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

The impact of obesity and sexual behavior on prostate cancer risk is mediated by testosterone levels: a mendelian randomization study and mediation analysis

Background

The relationship between obesity, sexual behavior, and prostate cancer (PCa) has been widely debated, contributing to a lack of understanding of its potential mechanisms and hindering the development of effective prevention measures.

Purpose

The aim of this study was to examine the causal effect of body mass index (BMI), age at first sexual intercourse (AFS), and bioavailable testosterone levels on PCa while also quantifying the potential roles of mediators.

Method

We conducted a Mendelian randomization (MR) study using summary statistics from genome-wide associations of BMI (152,893 European males), AFS (182,791 European males), bioavailable testosterone (184,205 European males), and PCa (79,148 cases, 61,106 controls, European ancestry). Inverse-variance weighted method, weighted median method, MR-Egger regression, Least Absolute Shrinkage and Selection Operator (LASSO), and outlier test were used for MR analyses. Reverse MR and mediation analysis were performed. Data analyses were conducted from December 2022 to July 2023.

Results

The results showed that genetic liability to BMI was protective of PCa (OR, 0.82; 95% CI: 0.74-0.91; P = 3.29 × 10-4). Genetic liability to later AFS (OR, 1.28; 95% CI: 1.08-1.53; P = 5.64 × 10-3) and higher bioavailable testosterone levels (OR = 1.11, 95% CI: 1.01-1.24, P = 0.04) were associated with an increased risk of PCa. All of these potential causal effects could only be forwarded and were not affected by prostate specific antigen (PSA) screening. After controlling for bioavailable testosterone levels, the causal impact of BMI and AFS on PCa was no longer significant. The mediation analysis suggested that the causal influence of AFS/BMI on PCa relied on bioavailable testosterone levels.

Conclusion

In conclusion, the difference between the univariable and multivariable MR results suggested that the causal influence of BMI and AFS on PCa relied on bioavailable testosterone levels. Further work is needed to identify other risk factors and to elucidate the specific mechanisms that underlie this causal pathway.

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

ABSTRACT

Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem-solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared with humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models, such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including postpublication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of large language models, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to affect it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.

Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800

The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify stones on computed tomography (CT) images and simultaneously classify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated CT images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy.