Clinical Integration of Artificial Intelligence in Urology with a Focus on Temporal Deep Neural Networks for Emphysematous Pyelonephritis| Chapter 3 | Newer Frontiers in Urology, Volume III

 

Artificial intelligence (AI) is transforming urology by facilitating quick, data-driven analysis in diagnosis and treatment. This chapter explores core AI concepts and their applications in urological conditions like kidney stones, bladder cancer, prostate cancer, and benign prostatic hyperplasia (BPH). Examples of models (such as convolutional neural networks and support vector machines) used in urology are used to teach general AI approaches (machine learning and deep learning). Automated MRI prostate cancer diagnosis (AUC ~0.96), ureteroscopic stone identification (CNN ~90% accuracy), and bladder tumour segmentation are significant achievements.

 

Additionally, the chapter highlights issues with dataset heterogeneity, sample size, and selection bias while briefly discussing the types of datasets used in AI-driven urology research, including imaging, clinical, and longitudinal data. A case study of an emerging multi-task deep neural network (t-MTDNN) for the prediction of emphysematous pyelonephritis (EPN) is included. Feature descriptions (SHAP) are provided for the t-MTDNN architecture and workflow, and its clinical impact and performance metrics are analysed.

 

In conclusion, the strengths and challenges of AI models are compared (Table 1) and prospective opportunities in AI-assisted urology are described with an emphasis on the therapeutic advantages (improved accuracy, efficiency) and limitations (data requirements, interpretability). Instead of replacing clinical judgement, urologists and healthcare organisations view AI as a clinical decision-support tool that can enhance workflow efficiency, support hospital-level adoption through interdisciplinary collaboration, and augment physician expertise.

 

 

Author(s) Details

Roshan Reddy
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Rajan Ravichandran
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Vivek Meyyappan
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Velmurugan Palaniyandi
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Hariharasudhan Sekar
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Sriram Krishnamoorthy
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-47485-93-0/CH3

Artificial Intelligence in Predictive Analytics of Patient Outcomes and Disease Management | Chapter 6 | An Overview of Disease and Health Research Vol. 2

Artificial Intelligence (AI) has revolutionised predictive analytics in healthcare, offering innovative approaches for patient outcome prediction and disease management. This review explores the role of AI-driven predictive models in early disease detection, prognosis estimation, and personalised treatment strategies. This review also discusses recent advancements, limitations, and future prospects of AI-powered predictive analytics in healthcare, emphasising its transformative potential in improving patient care and disease prevention. Machine learning (ML) algorithms, deep learning (DL) networks, and natural language processing (NLP) have significantly enhanced predictive capabilities by analysing vast datasets, including electronic health records (EHRs), genetic information, and real-time patient monitoring data. AI applications in disease management facilitate early intervention, optimise resource allocation, and improve clinical decision-making. However, challenges such as data privacy, model interpretability, and ethical considerations remain key concerns. Ensuring transparency and explainability in AI models is crucial to gaining clinician and patient trust while mitigating risks associated with biased or erroneous predictions.

 

Author (s) Details

Zaki Siddiqui
Department of Medicine, MLB Medical College, Jhansi, Uttar Pradesh, India.

 

Nirali D Vyas
GMERS Medical College, Vadnagar, Gujarat, India.

 

Soumya Kumar Acharya
Department of General Medicine, PGIMER and Capital Hospital, Bhubaneswar, Orissa, India.

 

Sanket Patel
GMERS Medical College, Vadnagar, Gujarat, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/aodhr/v2/5641