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Exploring Artificial Intelligence Biases in Predictive Models for Cancer Diagnosis.

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Most AI oncology studies exhibit bias and poor reporting, failing to meet ASCO

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • The American Society of Clinical Oncology (ASCO) established principles for responsible artificial intelligence (AI) use in oncology.
  • The adherence to these principles in published research remains largely unassessed.

Purpose of the Study:

  • To evaluate the presence of biases and the quality of studies on AI models for cancer diagnosis.
  • To assess compliance with ASCO's responsible AI principles.
  • To examine the impact of these factors on subsequent research applications.

Main Methods:

  • A systematic review of AI predictive models for cancer diagnosis published in an ASCO informatics journal.
  • Evaluation using 17 bias criteria aligned with ASCO principles and the CREML checklist for study quality.
  • Analysis of performance metrics and citation counts.

Main Results:

  • Nine studies were included, revealing common biases: environmental, life-course, contextual, provider expertise, and implicit bias.
  • Transparency, oversight, privacy, and human-centered AI application were the least adhered-to ASCO principles.
  • Only 22% of studies provided data access, and CREML checklist indicated methodological and reporting deficiencies.

Conclusions:

  • Most AI oncology studies exhibit biases and reporting flaws, limiting their applicability and reproducibility.
  • There is a significant gap in adherence to ASCO's responsible AI principles.
  • Recommendations include enhancing transparency, data accessibility, and adherence to international guidelines for reliable AI research in oncology.