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Summary

This study introduces a practical framework for urologists to develop artificial intelligence (AI) prediction tools using their own data. Clinicians can now build and deploy validated AI models for urological practice without extensive coding knowledge.

Keywords:
a urologist perspectiveai and machine learningartificial intelligence and medicinedata and analyticspredictive modelsurgical researchurology education

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Area of Science:

  • Urology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) and machine learning (ML) are revolutionizing urology.
  • Clinicians often lack the technical expertise to develop or evaluate AI predictive models.
  • Existing resources are typically data-science focused, not clinician-oriented.

Purpose of the Study:

  • To present a practical, clinician-focused framework for developing and deploying AI/ML predictive models in urology.
  • To empower urologists to utilize their existing data for AI tool creation.
  • To guide urologists through the entire AI model development lifecycle.

Main Methods:

  • A nine-part framework covering data appraisal, cleaning, model selection (logistic regression, Random Forest, XGBoost, Cox regression), splitting, cross-validation, performance evaluation (AUC, calibration, decision curve analysis).
  • Includes AI-assisted coding (Google Colab, LLMs), web deployment (Hugging Face, Gradio, GitHub, Render, Google Cloud), manuscript preparation (TRIPOD-AI), and ethical considerations.
  • Demonstrated by developing a prostate cancer prediction tool within 72 hours by an author without prior programming experience.

Main Results:

  • Successful development and public deployment of a validated multi-outcome prediction tool for high-risk prostate cancer.
  • The framework enabled a clinician, with no prior coding experience, to build and deploy the tool using free, open-source infrastructure.
  • The process was completed within 72 hours, showcasing the framework's efficiency and accessibility.

Conclusions:

  • AI-based clinical prediction tools are becoming increasingly accessible to urologists.
  • A systematic approach and structured datasets allow clinicians to build validated AI tools.
  • This framework democratizes AI model development, enabling urologists to contribute to AI-driven urological care without extensive coding.