How to Recognize Labeling Errors and Ask for Corrections in Medical Data Annotation

When you're working with medical data - like patient records, drug labels, or diagnostic images - a single wrong label can mean the difference between a correct diagnosis and a dangerous mistake. Labeling errors in datasets used to train AI models for healthcare aren’t just technical glitches. They’re patient safety risks. If a radiology image is mislabeled as "normal" when it shows a tumor, or a drug name is tagged as "ibuprofen" instead of "ibuprofen 800mg," the AI learns the wrong thing. And it will keep making that mistake, over and over.

What Labeling Errors Look Like in Medical Data

Labeling errors don’t always jump out. They’re often subtle, but their impact is huge. In medical datasets, these errors fall into clear patterns:

• Missing labels: An abnormality in an X-ray isn’t marked at all. This happens in 32% of object detection errors in medical imaging, according to Label Studio’s 2022 analysis.
• Wrong boundaries: A tumor is labeled, but the box drawn around it cuts off part of the lesion. This accounts for 41% of entity recognition errors in clinical text.
• Incorrect classification: A drug is labeled as "antibiotic" when it’s actually an antiviral. Misclassified entity types make up 33% of errors in medication datasets.
• Ambiguous or out-of-distribution examples: A note says "patient reports dizziness after taking med" - but the label says "adverse reaction" when the drug isn’t even in the approved list. These confuse models and account for 15-25% of text classification errors.
• Midstream tag changes: The team changes the label from "hypertension" to "high blood pressure" halfway through the project. Without version control, the dataset becomes a mess.

These aren’t random mistakes. They come from unclear instructions, rushed annotation, or lack of domain expertise. A 2022 study by TEKLYNX found that 68% of labeling errors in industrial and medical projects stemmed directly from vague or incomplete guidelines.

How to Spot Them - Even Without Coding

You don’t need to be a data scientist to find these errors. Here’s how to do it manually, using tools you already have access to:

1. Compare annotations side by side. Open two or three different annotators’ work on the same image or text. If one says "aspirin 81mg" and another says "aspirin," that’s a red flag. Disagreements mean inconsistency - and likely error.
2. Look for outliers. If 98% of the labels for a drug are "metformin," but one entry says "metformin 1000mg" while the rest are 500mg, check if that’s a typo or a valid variation. Unusual patterns often point to mistakes.
3. Use model predictions as a filter. If you’re using a tool like Datasaur or Argilla, run the dataset through a trained model. It will flag entries where the model is uncertain. These are your high-risk candidates. Even if the model isn’t perfect, its uncertainty tells you where to look.
4. Read the raw data aloud. For text labels, reading out loud helps catch mismatched terms. If the text says "patient allergic to penicillin" but the label is "no allergies," you’ve found an error.

Tools like cleanlab can automate this, but they require technical setup. For most teams working in pharmacy or clinical settings, manual spot-checking with these methods is faster, cheaper, and just as effective - especially when combined with peer review.

How to Ask for Corrections - Without Burning Bridges

Finding an error is only half the battle. Getting it fixed requires clear, respectful communication. Here’s how to do it:

• Don’t say "you made a mistake." Say: "I noticed this label might need a second look. Can we check the source document?"
• Include the evidence. Attach the original image, text snippet, or reference guideline. Example: "The patient’s chart says ‘ibuprofen 400mg,’ but the label says ‘ibuprofen 200mg.’ Could we verify?"
• Use a shared tracking system. Tools like Label Studio or Argilla let you flag items with comments. Use them. Don’t send a Slack message - log it in the system so it’s traceable and doesn’t get lost.
• Offer a correction, not just a complaint. Instead of just saying "this is wrong," suggest: "I think this should be labeled ‘anticoagulant’ based on the prescribing note. Can we confirm?"

People make errors because they’re tired, rushed, or unsure. Your job isn’t to blame - it’s to improve the system. Teams that use this approach see 50% faster correction turnaround and higher annotator retention.

Preventing Errors Before They Happen

The best way to fix labeling errors is to stop them before they’re made:

• Write clear labeling instructions. Include 3-5 real examples for every label. Don’t just say "label all drugs." Say: "Label all active pharmaceutical ingredients (e.g., metformin, lisinopril) - but not brand names like ‘Advil’ unless specified in guidelines."
• Use version-controlled guidelines. If you update the rules, document it. Name the version: "Labeling Guide v2.1 - Updated 12/1/2025."
• Run a pilot test. Have 3 annotators label 50 samples together. Review their results as a group. You’ll catch 80% of ambiguities before the full project starts.
• Train annotators on medical context. A person who doesn’t know the difference between "hypotension" and "hypertension" will mislabel 1 in 5 entries. Domain knowledge matters.

TEKLYNX’s 2022 study showed that teams using these practices reduced labeling errors by 47% in just two months.

Why This Matters More Than You Think

In healthcare, poor data quality doesn’t just slow down AI development - it endangers lives. A 2023 FDA guidance document explicitly requires that training datasets for AI medical devices include "systematic validation of labeling accuracy." If your dataset has 8% labeling errors - which is average - your AI model’s accuracy is capped at 92%, no matter how advanced it is. Professor Aleksander Madry from MIT put it bluntly: "No amount of model complexity can overcome bad labels."

And it’s not just about accuracy. Regulatory bodies, insurers, and hospitals are starting to audit data quality. If you’re building a drug interaction checker or a medication alert system, your data pipeline will be scrutinized. Clean, verified labels aren’t a nice-to-have - they’re a compliance requirement.

What to Do Next

Start small. Pick one dataset - maybe your most critical one, like medication allergy labels or prescription transcription. Run a manual review using the methods above. Flag 10-20 errors. Correct them. Track how the model’s performance improves. You’ll see results fast.

Then, build a simple checklist:

1. Are labeling instructions clear and example-rich?
2. Are annotators trained on medical terminology?
3. Are multiple annotators reviewing high-risk items?
4. Are corrections logged and tracked?
5. Is there a process to update guidelines when new cases come up?

Do this once a month. You’ll turn labeling from a chaotic, error-prone task into a reliable, auditable process.