The role of artificial intelligence in the future of urogynecology

Current applications and future perspectives of artificial intelligence in functional urology and neurourology: how far can we get?

In the last few years, the scientific community has seen an increasing interest towards the potential applications of artificial intelligence in medicine and healthcare. In this context, urology represents an area of rapid development, particularly in uro-oncology, where a wide range of applications has focused on prostate cancer diagnosis. Other urological branches are also starting to explore the potential advantages of AI in the diagnostic and therapeutic process, and functional urology and neurourology are among them. Although the experiences in this sense have been quite limited so far, some AI applications have already started to show potential benefits, especially for urodynamic and imaging interpretation, as well as for the development of AI-based predictive models for treatment response. A few experiences on the use of ChatGPT to answer questions on functional urology and neurourology topics have also been reported. Conversely, AI applications in functional urology surgery remain largely unexplored. This paper provides a critical overview of the current evidence on this topic, highlighting the potential benefits for the diagnostic workflow, therapeutic evaluation and surgical training, as well as the current limitations that need to be addressed to enable the integration of this tools in the clinical practice in the future.

The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects

Artificial intelligence (AI) is the new medical hot topic, being applied mainly in specialties with a strong imaging component. In the domain of gynecology, AI has been tested and shown vast potential in several areas with promising results, with an emphasis on oncology. However, fewer studies have been made focusing on urogynecology, a branch of gynecology known for using multiple imaging exams (IEs) and tests in the management of women's pelvic floor health. This review aims to illustrate the current state of AI in urogynecology, namely with the use of machine learning (ML) and deep learning (DL) in diagnostics and as imaging tools, discuss possible future prospects for AI in this field, and go over its limitations that challenge its safe implementation.

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023

INTRODUCTION

A "Think Tank" at the International Consultation on Incontinence-Research Society meeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data.

METHODS

Examples of the use of ML applied to data from uroflowmetry, pressure flow studies and imaging were presented. The advantages and limitations of ML were considered. Recommendations made during the subsequent debate for research studies were recorded.

RESULTS

ML analysis holds great promise for the kind of data generated in urodynamic studies. To date, ML techniques have not yet achieved sufficient accuracy for routine diagnostic application. Potential approaches that can improve the use of ML were agreed and research questions were proposed.

CONCLUSIONS

ML is well suited to the analysis of urodynamic data, but results to date have not achieved clinical utility. It is considered likely that further research can improve the analysis of the large, multifactorial data sets generated by urodynamic clinics, and improve to some extent data pattern recognition that is currently subject to observer error and artefactual noise.

Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.