John Snow Labs is emerging as the clear industry leader for state-of-the-art NLP in healthcare.
We cannot recommend a better way to apply the most current, accurate, and scalable technology to your natural language understanding challenges today.
By all accounts, John Snow Labs has created the most accurate software in history to extract facts from unstructured text.
Make 4-6X Fewer Errors than AWS,
Azure, or GCP
State-of-the-Art Medical Large Language Models
Clinical Note Summarization
is 30% more accurate than BART, Flan-T5 and Pegasus.
Clinical Entity Recognition
John Snow Labs’ models make half the errors that ChatGPT does.
Extracting ICD-10-CM Codes
is done with a 76% success rate versus 26% for GPT-3.5 and 36% for GPT-4.
What’s in the Box
- Document Classification
- Entity Disambiguation
- Contextual Parsing
- Patient Risk Scoring
- Deep Sentence Detector
- Medical Spell Checking
- Medical Part of Speech
- Terminology Mapping
Lisinopril 10 MG
- Name Consistency
- Gender Consistency
- Age Group Consistency
- Format Consistency
- Entities by Prompt
- Relations by Prompt
- Classification by Prompt
- Relative Data Extraction
Fever and sore throat
No stomach pain
Father with Alzheimer
1,000+ Pretrained Models
Signs, Symptoms, Treatments, Findings, Procedures, Drugs, Tests, Labs, Vitals, Sections, Adverse Effects, Risk Factors, Anatomy, Social Determinants, Vaccines, Demographics, Sensitive Data
Clinical Trial Design, Protocols, Objectives, Results; Research Summary & Outcomes; Organs, Cell Lines, Organisms, Tissues, Genes, Variants, Expressions, Chemicals, Phenotypes, Proteins, Pathogens
Trainable & Tunable
Scalable to a Cluster
State Of The Art Accuracy
Production-Grade, Fast & Trainable Implementation of State-of-the-Art Biomedical NLP Research
Deeper Clinical Document Understanding Using Relation Extraction. Hasham Ul Haw, Veysel Kocaman and David Talby, 2022
Mining Adverse Drug Reactions from Unstructured Mediums at Scale. Hasham Ul Haw, Veysel Kocaman and David Talby, 2022
Biomedical Named Entity Recognition at Scale. Authors: Veysel Kocaman and David Talby, 2020
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP. Veysel Kocaman and David Talby, 2021
Accurate Clinical and Biomedical Named Entity Recognition at Scale. Veysel Kocaman and David Talby, 2022
Biomedical Named Entity Recognition in Eight Languages with Zero Code Changes. Veysel Kocaman, Gursev Pirge, Bunyamin Polat, David Talby, 2022
Some Al companies stand out via outstanding academic validation; some via successful customers and deployments; and yet others by using Al for good. John Snow Labs is utterly unique in going all three.
Proven success across healthcare
For Data Scientists
Install software, try Python libraries, notebooks, and models on your own infrastructure