Healthcare & Biotechnology

AI & ML solutions

Artificial Intelligence has disrupted the healthcare industry by performing tasks more efficiently than a human can. Machine Learning can be used for various applications, such as patient data analysis, drug discovery, in-patient care, and hospital management. Embracing AI helps hospitals in early disease detection, connecting patients with the right resources and extract meaning from unstructured data assets.

COMPUTER VISION - Image Classification

Medical Imaging & Detection

Deep Learning has demonstrated remarkable progress in image-recognition functions. Medical imaging is one of the most performed tasks using this technology. AI methods excel at recognizing complex patterns in the images and providing assessments of medical characteristics. AI models can be an effective tool for analyzing medical ailments like cardiovascular abnormalities, lung diseases like pneumonia, the development of tumors and melanoma, and checking for fractures from high-resolution medical imagery. This helps to provide timely treatments to patients.

Medical Imaging & Detection

NATURAL LANGUAGE PROCESSING - Named Entity Recognition

Biomedical Named Entity Recognition

Bio-entity extraction is a core task of information extraction from medical literature. Examples of such entities include names of genes, proteins, location of activity etc. With the increase in biomedical record content, human annotators find this extraction task tedious. AI and Deep Learning can automate identifying and classifying bio-medical terms into predefined categories, from blocks of unstructured text data.

Biomedical Named Entity Recognition

NATURAL LANGUAGE PROCESSING - Named Entity Recognition

Electronic Records Analysis

For healthcare services, analyzing electronic medical records is crucial in making the correct clinical decisions for their patients. A large amount of patient information is recorded in the form of free-text notes by physicians. Analyzing this unstructured text data is tedious, but using Natural Language Processing can automatically extract features or risk factors of patient health from these notes. Apart from clinical data, notes about patients’ emotional wellbeing and their speech transcripts can be analyzed to get insights about their mental health as well. AI extracts clinical information that would normally be missed using manual analysis methods.

Electronic Records Analysis

More Case Studies and Blogs

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How Artificial Intelligence Is Making Healthcare Better

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Computer Vision Applications in Healthcare - 2020

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Computer Vision in Artificial Intelligence

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