Artificial Intelligence in Healthcare

Streamline unstructured data to overcome everyday challenges. Simplify data analysis, derive greater insights, and deliver personalized care to patients with healthcare NLP.

Healthcare ai

The strongest clinical NLP APIs that deliver speed and simplicity

Clinical nlp apis

Extracting meaningful clinical entities from unstructured clinical data

PHI Redaction

API for De-identification of Protected Health Information (PHI), that strips all “direct identifiers” i.e all information that can be used to identify the patient.

SnoMed & RxNorm

Implement an API for medical billing and coding that utilizes Natural Language Processing (NLP) to scrutinize and derive Snomed CT and RxNorm identifiers.



Clinical API that inspects laboratory test orders and results. Unlock medical laboratory observations for identifiers, names and codes using our NLP.


Highly accurate API for medical coding that extracts billable ICD-10-CM and PCS codes from patient encounter documents at the click of a button.

Named Entity Recognition (NER)

Clinical NLP API that extracts medical entities, its context and relationship from large chunks of unstructured clinical data using Deep Learning NLP Models.

Custom APIs

Tailor-made for personalized needs. Do you have a specific requirement? HealthcareNLP’s team of researchers and engineers will build it, especially for you.

Use Cases

Clinical Entity Recognition
Clinical entity recognition
Oncology models
Relation extraction
Radiology models
Assertion status

Success Stories

Oncology Data Enhancement: Licensing, De-identification, & Annotation

The client, a prominent healthcare entity, needed a sophisticated NLP system to handle a large amount of oncology records. This case study details our work in improving the client’s research through precise data annotation, strict de-identification, and NLP implementation, all in compliance with HIPAA regulations.

Problem: The project combined expert clinical documentation analysis, medical entity identification, and privacy adherence to HIPAA, requiring both technical and strategic annotation skills.

Solution: Delivered 10,000 de-identified, labeled records for the client’s NLP model, adhering to HIPAA standards and enhancing their oncology research and patient care outcomes.

Oncology nlp case study

Shaip’s Healthcare AI Benefits



Our NLP model has high accuracy in processing medical text.



No coding or NLP knowledge is needed. Get started in a matter of seconds.



Access simplified NLP implementation and usage.



Adapt and fine-tune to your organization's unique needs and requirements.



Integrate it with your existing healthcare systems and workflows seamlessly.

Highest Standards of Privacy & Security

Our Natural Language Processing (NLP) technology is designed and implemented with stringent measures to ensure complete safety and security.

  • State-of-the-art encryption protocols
  • Secured data storage
  • Adherence to HIPAA and GDPR
  • Transparent privacy policy
Shaip privacy & security
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Healthcare NLP is the application of Natural Language Processing technologies in the healthcare sector to extract, process, and understand complex medical data from various sources, including electronic health records, clinical notes, research papers, and patient feedback, among others.

NLP in healthcare can be used for disease prediction and diagnosis, treatment pathway recommendations, understanding patient sentiment, automating data entry, optimizing billing processes, health monitoring and alerting, and much more.

NLP can help healthcare providers to better understand a patient’s history, symptoms, and concerns, leading to more accurate diagnoses and personalized treatment plans. It also allows for the efficient processing of large amounts of data, facilitating research, predictive modeling, and proactive healthcare management.

Some challenges include dealing with unstructured and non-standardized medical data, ensuring data privacy and security, overcoming language and cultural barriers, and integrating NLP systems with existing healthcare IT infrastructure.

Healthcare NLP must comply with all relevant data privacy laws and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. This can involve anonymizing data, obtaining patient consent, and implementing strict data security measures.

Yes, Healthcare NLP can be a valuable tool in telemedicine by facilitating remote patient monitoring, interpreting patient’s spoken or written language in real-time, and helping physicians to diagnose and treat patients remotely.

NLP can assist in medical research by automating the process of literature review and data extraction, identifying patterns and trends in large datasets, and helping researchers to make sense of complex medical terminology.

Yes, by analyzing patterns in patient data and medical literature, NLP algorithms can predict the likelihood of diseases. These predictive models can assist physicians in early detection and preventive care.

NLP can extract and interpret important clinical information from EHRs, such as diagnoses, symptoms, and treatments. This can help healthcare providers to make better use of EHR data, leading to improved patient outcomes.

The future of Healthcare NLP may involve more sophisticated understanding of medical language, real-time processing of patient data, and seamless integration with other healthcare technologies. It holds the potential to revolutionize patient care, medical research, and healthcare administration.