Dr. Werneburg on machine learning and AI’s potential in urology

Opinion
Video

"We're really enthusiastic on the overactive bladder end and on the antibiotic resistance end, but much more broadly, [AI is] set up to help us counsel our patients better and help us to improve outcomes for our patients as well," says Glenn T. Werneburg, MD, PhD.

In this video, Glenn T. Werneburg, MD, PhD, discusses potential future applications of machine learning and artificial intelligence in urology. He presented the abstracts “Machine learning algorithms demonstrate accurate prediction of objective and patient-reported response to botulinum toxin for overactive bladder and outperform expert humans in an external cohort” and "Machine learning algorithms predict urine culture bacterial resistance to first line antibiotic therapy at the time of sample collection” at the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction 2024 Winter Meeting in Fort Lauderdale, Florida. Werneburg is a urology resident at Glickman Urological & Kidney Institute at Cleveland Clinic, Cleveland, Ohio.

Transcription:

What are some next steps regarding machine learning algorithms and urologic conditions?

Regarding these studies, specifically, we have some additional efforts in terms of validation. We want to validate these prospectively and establish how generalizable they are on different patient subsets. Those are aspects of the work that are ongoing right now. But thinking more broadly, I think that these 2 cases illustrate that artificial intelligence and machine learning is really poised to help us define which treatment for which patient is the most suitable. It's a large step forward toward precision medicine. In urology alone, we have many, many cases wherein we have different treatments that might work for different patients. But we don't know which treatment is best for which patient. We often have some idea; we have pretty good clinical judgment and lots of good data with clinical trials. But still, we arrive at a situation where there are different therapies that may work similarly well, but we don't know which patient is best suited for which therapy and which therapy is best suited for which patient. AI can help us with this. Just thinking about prostate cancer, for example. We have many different treatment modalities. We have prostatectomy, different types of radiation therapy. We now have different modalities for focal therapy, where only a portion of the prostate is treated. We have active surveillance, which is monitoring the condition with the idea to intervene when and if needed, and algorithms such as this can help us identify which modality will be best for which patient. We're really enthusiastic on the overactive bladder end and on the antibiotic resistance end, but much more broadly, it's set up to help us counsel our patients better and help us to improve outcomes for our patients as well.

This transcription was edited for clarity.

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