AI for healthcare

From improving patient health to clinical trials, Snorkel Flow provides healthcare innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.

Request a demo

AI For Healthcare

From improving patient health to clinical trials, Snorkel Flow provides healthcare innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.

Request a demo



Case study

Fortune 500 biotech

A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read more



Problem

Building AI applications to extract entities requires high domain expertise and large amounts of labeled training data, which is expensive and time consuming.

Solution

With Snorkel Flow they built a custom model with 99.1% accuracy by adjusting label schema and re-labeling programmatically.

Results

With Snorkel Flow, this biotech giant programmatically labeled ~300K documents in minutes versus using manual labeling, all while saving $10M in costs.

$10M

saved on labeling for extraction

99.1%

accuracy on complex ML pipeline

1 day

vs. 1 year to adjust label schema


Case Study

Fortune 500 Biotech

A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read more



Problem

Building AI applications to extract entities requires high domain expertise, and large amounts of labeled training data, which is expensive and time consuming.

$10M

saved on labeling for extraction

Solution

Used Snorkel Flow to build a custom model with 99.1% accuracy by adjusting label schema and re-labeling done in hours.

99.1%

accuracy on complex ML pipeline

Results

With Snorkel Flow, this biotech giant programmatically labeled ~300k documents in minutes versus using manual labeling, all while saving $10M in costs.

1 day

vs. 1 year to adjust label schema


Case Study

Fortune 500 Biotech

A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read More



Problem

Building AI applications to extract entities requires high domain expertise, and large amounts of labeled training data, which is expensive and time consuming.

Solution

Used Snorkel Flow to build a custom model with 99.1% accuracy by adjusting label schema and re-labeling done in hours.

Results

With Snorkel Flow, this biotech giant programmatically labeled ~300k documents in minutes versus using manual labeling, all while saving $10M in costs.

$10M

saved on labeling for extraction

99.1%

accuracy on complex ML pipeline

1 day

vs. 1 year to adjust label schema

Data-centric AI

Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
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Accelerated

Save time and costs by replacing manual labeling with rapid, programmatic labeling.
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Adaptable

Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
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Collaborative

Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
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Accurate

Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
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Governable

Version and audit data like code, leading to more responsive and ethical deployments.
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Private

Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.

Use cases

AI solutions for healthcare

AI applications built using Snorkel Flow create speed, ease and accuracy for your healthcare needs with custom cutting-edge models and expertise. Rapid results, for a fraction of the cost.
Improving patient outcomes
Analyze patient health records to detect anomalies and find potentially problematic medical conditions, as well as assist diagnosis and treatment recommendations.
Claims processing
Identify entities in patient records and recognize insured persons, loss amount, and policyholder information to process claims faster or monitor potentially spurious claim behavior to detect subrogation claims.
Enhancing research
Automate data extraction from clinical trial records for digital pathology, classify medical papers by topic to improve research access, and classify patient records to identify actionable clinical trial candidates.

A radically new approach to AI

Conventional AI approaches rely on generic third-party models, or brittle rule-based systems, or armies of human labelers. With Snorkel Flow, programmatically labeling unlocks a new workflow that accelerates AI app development.

With Snorkel Flow

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Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
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Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
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Label thousands of data points programmatically in hours while keeping your data in-house and private.

With conventional approaches

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Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
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Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
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Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.

The platform for data-centric AI development

The platform for data-centric AI development

Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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September 20, 2021
Snorkel AI welcomes industry leaders to the team

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August 9, 2021
This hot startup is now valued at $1 billion for its A.I. skills

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February 24, 2021
The Data-First Enterprise AI Revolution

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July 14, 2020
Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning

Blog
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February 4, 2022
Making Automated Data Labeling a Reality in Modern AI

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Date: Jan 25, 2022
The Principles of Data-Centric AI Development

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Date: Jan 5, 2022
Meet the Snorkelers

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Date: Jul 9, 2021
How to Use Snorkel to Build AI Applications

Research
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2022
Universalizing Weak Supervision

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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records

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2017
Rapid Training Data Creation with Weak Supervision

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2016
Data Programming: Creating Large Datasets Quickly

Customer Stories
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February 26, 2022
Genentech used Snorkel Flow to extract information from clinical trials

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February 18, 2022
Google used Snorkel to build and adapt content classification models

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2019
Intel used Snorkel to accelerate sales and marketing agents

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2019
Apple built a Snorkel-based system to answer billions of queries in multiple languages

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Let’s connect

Speed time to value, reduce costs, and unlock more AI possibility with the Snorkel Flow platform.
Request a demo