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Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.

Henrik Olsson1, Kimmo Kartasalo2, Nita Mulliqi2

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. henrik.olsson@ki.se.

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Summary

Conformal prediction enhances artificial intelligence (AI) reliability in prostate cancer diagnosis. This method flags unreliable AI predictions, significantly improving patient safety by reducing diagnostic errors.

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Area of Science:

  • Medical Artificial Intelligence
  • Computational Pathology
  • Biostatistics

Background:

  • Artificial intelligence (AI) systems can produce unreliable predictions when encountering data outside their training distribution.
  • Histopathological diagnosis and grading of prostate biopsies are critical for patient management but susceptible to AI errors.
  • Ensuring the reliability of AI in medical diagnostics is paramount for patient safety.

Purpose of the Study:

  • To demonstrate the utility of conformal prediction for detecting unreliable AI predictions in prostate biopsy analysis.
  • To evaluate the impact of conformal prediction on the accuracy and safety of AI-driven histopathological diagnoses.

Main Methods:

  • Digitization and analysis of 7788 prostate biopsies for training and 3059 for testing from the STHLM3 diagnostic study.
  • Implementation of conformal prediction to identify and flag AI predictions with low confidence.
  • Evaluation of AI performance with and without conformal prediction on both internal and external datasets, including small sample validation.

Main Results:

  • Conformal prediction reduced cancer diagnosis errors to 0.1% (1 in 794) compared to 2% (14 errors) without it.
  • When presented with new data, 22% of predictions were flagged as unreliable, indicating effective detection of out-of-distribution samples.
  • In cases of atypical prostate tissue, conformal prediction decreased errors to 2% (3 errors) from 25% (44 errors), flagging 80% as unreliable.

Conclusions:

  • Conformal prediction significantly enhances the reliability and safety of AI systems in histopathological diagnosis.
  • The method effectively identifies systematic differences in external data, preventing erroneous diagnoses.
  • Conformal prediction is a valuable tool for increasing patient safety in AI-assisted medical diagnostics.