How to Recognize Labeling Errors in Medical Data and Request Corrections

When you're working with medical data - whether it's patient records, diagnostic images, or clinical notes - the labels attached to that data can make or break an AI model. A single mislabeled X-ray or incorrectly tagged symptom can lead to a system missing a tumor, misdiagnosing a condition, or recommending the wrong treatment. Labeling errors aren't just typos. They’re systemic problems that quietly degrade performance, often going unnoticed until a model fails in production. The good news? You don’t need to be a data scientist to spot them. You just need to know what to look for and how to ask for corrections the right way.

What Labeling Errors Actually Look Like in Medical Data

Labeling errors in medical datasets aren’t random. They follow patterns. In a 2024 study of 12,000 annotated medical images from Australian hospitals, researchers found that 41% of errors involved incorrect boundaries - meaning a tumor was labeled as smaller or larger than it actually was. Another 33% were outright misclassifications: a benign lesion marked as malignant, or pneumonia labeled as a normal chest X-ray. And 26% of cases? The abnormality was simply missing from the annotation entirely.

In text data, like discharge summaries or doctor’s notes, errors show up differently. Entity recognition systems often fail because a patient’s medication isn’t tagged at all, or a drug name is labeled as a condition. For example, "aspirin" might be tagged as a symptom instead of a medication. Or worse - a drug like warfarin gets labeled as "anticoagulant," which is correct, but the system then ignores all other anticoagulants because the training data never included them.

These aren’t just "mistakes." They’re data quality failures with real-world consequences. A 2023 analysis from the Royal Melbourne Hospital showed that models trained on datasets with uncorrected labeling errors had a 22% higher false-negative rate in detecting early-stage sepsis. That’s not a bug. That’s a safety risk.

How to Spot These Errors Yourself

You don’t need fancy tools to catch errors. Start with the basics:

• Check for missing annotations. Look at a sample of 20-50 records. Are there obvious abnormalities that weren’t labeled? If a patient has a documented skin rash but no annotation in the image or text, that’s a red flag.
• Look for inconsistent tagging. Is "hypertension" sometimes labeled as "high blood pressure"? Are different annotators using different terms for the same thing? Inconsistency breeds confusion in models.
• Watch for extreme class imbalance. If 95% of your labeled cases are "normal" and only 5% are "abnormal," the model will learn to ignore the rare cases. That’s not a labeling error per se - but it’s often caused by annotators avoiding rare cases because they’re hard to identify.
• Compare labels with source documents. If you’re working with clinical notes, pull up the original EHR entry. Does the label match what was written? If the note says "patient reports chest pain for 3 days," but the label says "acute MI," that’s a mismatch. The annotation added information not in the source.
• Spot ambiguous cases. Is there a case where two labels could both make sense? For example, a lung nodule that’s too small to confidently call benign or malignant? If annotators are unsure, the model will be too.

One real example from a Sydney-based diagnostic lab: An AI model was trained to detect diabetic retinopathy from retinal scans. The annotators were told to label "any visible microaneurysms." But some annotators only labeled large ones, while others flagged tiny dots. The model ended up over-predicting microaneurysms because it learned to see them everywhere. The fix? A new guideline with clear examples - and a re-annotation of the first 1,000 images.

How to Ask for Corrections Without Being Ignored

Most labeling teams are overwhelmed. If you just say, "This is wrong," you’ll get ignored. You need to make it easy for them to fix it.

• Be specific. Don’t say: "The labels are bad." Say: "In image #4823, the left lung nodule is labeled as size 4mm, but the scale bar shows it’s actually 8mm. The annotation box is too small by 50%.
• Provide context. Attach the original clinical note or radiology report. Say: "This matches the radiologist’s report dated 12/15/2025, which states: '2.1 cm solid nodule in right upper lobe.'"
• Use a template. Create a simple form:
  1. Dataset ID:
  2. Annotation ID:
  3. Expected label:
  4. Current label:
  5. Evidence: [attach screenshot or document excerpt]
  6. Why this matters: [e.g., "This could cause false negatives in early cancer detection"]
• Don’t blame. Say: "I noticed this might be an inconsistency. Could we review it?" Not: "You labeled this wrong."

At the Royal Adelaide Hospital, a clinical informatics team started using this template. Within six weeks, their labeling error rate dropped from 11.4% to 3.1%. Why? Because the annotators felt supported, not attacked.

Tools That Help - and Which Ones to Use

You don’t have to do this manually every time. Tools exist to flag errors before they become problems.

• cleanlab - This open-source tool uses statistical methods to find likely mislabeled examples. It works best with classification tasks like diagnosing pneumonia or classifying drug reactions. It doesn’t tell you what the correct label is - just which ones are probably wrong. You still need a human to confirm.
• Argilla - A web-based platform that lets you review flagged errors directly in your browser. It integrates with cleanlab and lets you correct labels in place. Ideal for teams without coding skills.
• Datasaur - Built for annotation teams. It has a built-in error detection feature that flags inconsistencies as you go. Great if you’re labeling text data like discharge summaries or patient surveys.

But here’s the catch: none of these tools work unless you feed them clean data. If your training set is full of errors, the tool will just flag more errors - and you’ll drown in noise. Start small. Pick one dataset. Fix the top 10% of errors. Then retrain. You’ll see results faster than you think.

Why This Matters More Than You Think

The FDA now requires that any AI system used in medical diagnosis must include documentation of data quality controls - including label error detection and correction. That’s not a suggestion. It’s a regulation.

In Australia, the Therapeutic Goods Administration (TGA) is moving in the same direction. If your organization is building or using AI for clinical decision-making, you’re already under pressure to prove your data is reliable.

But beyond compliance, there’s ethics. A mislabeled label can mean a patient gets the wrong treatment. Or no treatment at all. The cost of a labeling error isn’t just a lower accuracy score. It’s a broken trust.

What to Do Next

Start today:

1. Pick one dataset you’re working with - even if it’s small.
2. Review 20 random samples manually. Look for the five error types above.
3. Write up three clear correction requests using the template.
4. Share them with your annotation team. Don’t ask for a full re-annotation. Just ask: "Can we fix these three?"
5. Track the change in model performance after the fix.

Most teams don’t realize how much they’re losing to sloppy labels. You don’t need to fix everything. Just fix the ones that matter. One corrected label can mean one patient gets the right care.