Assertion Supported AI assessment confidence: 90% certainty 4/5 debate potential 2/5

Turakhia: Neural networks replicate human error patterns in EKG and imaging studies

Mintu Turakhia · a16z Podcast | Putting AI in Medicine, in Practice · Jan 2, 2019 · at 10:49

Mintu Turakhia, cardiologist and electrophysiologist, discusses how AI error profiles in diagnostic imaging and EKG analysis mirror human diagnostic errors rather than making erratic mistakes.

0:00 / 0:13exact quote · 13.7s

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“Some of the most promising aspects of the imaging studies and the EKG studies are that the confusion matrices, the way humans misclassify things is recapitulated by the convolutional neural networks.”

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The claim, plainly

Deep convolutional neural networks used in EKG and medical imaging studies replicate the same error and misclassification patterns as human physicians.

a quote alone can be ambiguous, so this restates it with the references and context of the conversation around it resolved. AI-written; the tape below the player is the source.

Assessment

Dr. Mintu Turakhia's claim that deep convolutional neural networks (CNNs) in medical imaging and EKG studies recapitulate human physician misclassification patterns is accurate. In medical imaging, Esteva et al. (Nature, Feb 2017) demonstrated that CNN confusion matrices mirrored dermatologists' errors, particularly between benign and malignant melanocytic lesions. In EKG analysis, Hannun et al. (co-authored by Turakhia) demonstrated in a July 2017 preprint—subsequently published in Nature Medicine in January 2019—that a 34-layer CNN's confusion matrices mirrored cardiologist error patterns (e.g., confusing Type 1 and Type 2 second-degree AV block). Although the EKG study was an un-peer-reviewed preprint at the exact time of the podcast, its peer-reviewed publication in January 2019 confirmed the statement within the horizon.

Esteva et al. (Nature, Feb 2017) demonstrated that CNN skin cancer classification confusion matrices mirrored dermatologist error patterns.
Hannun et al. (arXiv July 2017; Nature Medicine Jan 2019) demonstrated that deep CNN ECG confusion matrices mirrored cardiologist error patterns across 12 rhythm classes.

Checked against

[1] stanford.edu [2] irhythmtech.com [3] nih.gov [4] gwern.net [5] ai4health.io [6] google.com [7] ovid.com [8] reviewofophthalmology.com

"Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks" "confusion matrix" 2017 "Gulshan" JAMA 2016 "confusion matrix" OR "misclassification" OR "error" Mintu Turakhia current position title 2025 2026 Esteva Nature 2017 skin cancer deep learning confusion matrix physician errors deep learning ECG EKG Rajpurkar Turakhia Ng Cardiologist level arrhythmia detection

assessed as of 2026-07-31

This assessment is an AI reading of a public statement, made against the cited references with the confidence shown. It can be wrong. The quote, the tape and the references are all on this page; if something is off, use report an error below. full disclaimer →

Mentioned

convolutional neural networks EKG studies Mintu Turakhia

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Turakhia: Neural networks replicate human error patterns in EKG and imaging studies

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