Data annotation is an important part of the artificial intelligence (AI) lifecycle. Machine learning algorithms, especially computer vision solutions, require massive amounts of precisely labeled training data in order to accurately interpret real-world data. Healthcare innovators often turn to doctors for data labeling help but that's not the most efficient or cost-effective option. Here are four reasons that doctors shouldn't waste time on tedious data labeling and a solution to accelerate data processing pipelines. Read CloudFactory's Guide to Medical AI to discover how high-quality data is transforming healthcare and how CloudFactory can help you liberate doctors while accelerating AI development. https://www.cloudfactory.com/medical-ai-guide

1. 4 Reasons
Doctors Shouldn’t
Annotate Data for
Medical AI

2. Doctors are already
doing the work of 10
people.
Even before the COVID-19 pandemic set in, the U.S. faced a growing
shortage of doctors and other medical care providers. According to
the Association of American Medical Colleges, the U.S. will face a
shortage of up to 122,000 physicians by 2032.
AI reduces time-consuming, repetitive tasks and supports decision-
making in areas like clinical diagnostics, robot-assisted surgery, and
patient monitoring. AI adoption may even expand employment and
increase wages.
#1

3. You have to annotate
how many images &
videos?
Every year in the U.S., patients undergo 40 million MRI scans, 80+
million CT scans, and 152+ million X-rays. And millions of other
images are generated through other means, like operating room
robots and medical research and development.
It takes upwards of 800 hours to annotate one hour of video for
training data. Data-hungry computer vision models require a lot of
high-quality, precisely labeled data. Lives depend on medical AI and
quality data requires focused labelers.
#2

4. Doctors should be
focused on patients.
Your internal team might know how to identify cancer cells, but that
doesn’t mean they should label data. Dropping tedious annotation
chores on medical professionals leads to discontent, burnout, and,
eventually, compromised training data quality.
AI won’t replace doctors. AI can’t empathize, respond with
compassion, or use intuition. Doctors should adopt AI tools, not train
them one bounding box at a time.
#3

5. Time is money. And
innovation can’t wait.
A medical AI company contracted several physicians to label data
during their off-work hours. Each medical consultant charged around
$150 an hour and the doctors were able to annotate only 10K images
in 6 months—for a hefty fee.
Complex deep learning algorithms often require 100K+ pieces of
precisely labeled training data. It would take those consulting
physicians 5+ years to complete what CloudFactory’s annotation
workforce can do in weeks.
#4

6. Hi.
We’re CloudFactory
We have 10+ years of experience providing data labeling for
500+ organizations across the globe. Our expert medical AI
annotation workforce is committed to delivering high-quality
data to train, optimize, and refine your medical AI models.
Learn more about outsourcing healthcare data labeling in our
guide:
Medical AI Breakthroughs: How High-Quality Data is
Transforming Healthcare and Medicine.
Read the Guide to Medical AI