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Bladder Cancer and Artificial Intelligence: Emerging Applications.

Mark A Laurie1, Steve R Zhou2, Md Tauhidul Islam3

  • 1Department of Urology, Stanford University School of Medicine, 453 Quarry Road, Mail Code 5656, Palo Alto, CA 94304, USA; Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive Room G204, Stanford, CA 94305-5847, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA; Institute for Computational and Mathematical Engineering, Stanford University School of Engineering, Stanford, CA 94305, USA.

The Urologic Clinics of North America
|November 9, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for improving bladder cancer management, including early detection and risk stratification. Further research is needed to confirm AI

Keywords:
AI-assisted diagnosisArtificial intelligenceBladder cancerDeep learningImage processingOutcome predictionTreatment planningUrology

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Area of Science:

  • Urology
  • Oncology
  • Medical Informatics

Background:

  • Bladder cancer presents significant patient and healthcare burdens due to its commonality and heterogeneity.
  • Current clinical approaches suffer from imprecision, leading to missed diagnoses, inadequate treatments, and disease progression.
  • Key challenges include the need for effective early detection, accurate risk stratification, and managing treatment side effects.

Purpose of the Study:

  • To explore the potential of artificial intelligence (AI) in addressing unmet needs in bladder cancer care.
  • To evaluate how AI can enhance early detection, risk stratification, treatment planning, quality assessment, and outcome prediction.
  • To review the current state of AI in bladder cancer management and identify areas for future research.

Main Methods:

  • Review of current literature on bladder cancer management and artificial intelligence applications.
  • Analysis of the limitations of existing clinical paradigms.
  • Identification of specific areas within bladder cancer care where AI can offer solutions.

Main Results:

  • Artificial intelligence demonstrates potential across multiple facets of bladder cancer management.
  • AI applications may improve early detection accuracy and refine patient risk stratification.
  • AI tools could optimize treatment planning and enhance outcome prediction for bladder cancer patients.

Conclusions:

  • Artificial intelligence holds significant promise for revolutionizing bladder cancer diagnosis and treatment.
  • AI can potentially overcome limitations in current clinical practices, improving patient outcomes.
  • Continued research and validation are essential to integrate AI effectively into clinical decision-making for bladder cancer.