Deep Learning applications are looking for more data every day. And the more quality and high labeled data a developer feeds into an AI model the more accurate is the inferences. However, creating datasets is one of the biggest challenges developers and data scientists are facing today while building their machine learning models and Artificial Intelligence initiatives.

But today there are a lot of startups that are rising and have successfully created a web platform that can help the researchers as well as companies manage and analyze data labels in workflow and also utilize AI-enabled segmentation tools for improving the quality of their training data sets. Hence when the labels are accurate Artificial Intelligence models launch quickly and reach a high level of accuracy in no time.

A lot of Artificial Intelligence and Machine Learning startups are seamlessly building new initiatives for the healthcare sector. For example, how NVIDIA T4 GPU has its inference in Google Cloud contributed in Healthcare radiology by speeding up the customer labeling by 10x and reduced the labeling error rate by 15% or more.

Let’s dive deep into understanding how data annotation and machine learning are helping in the advancement of the modern Health Sector:

• Trainingdata.io

Trainingdata.io chose to develop its platform on a cloud-based platform used to scale up and down usage seamlessly based on client demand. Companies are making the most of this tool which can choose whether to use the interface online or connected to the cloud in the backend or also use a containerized application for running on their own premises of the GPU system.

Artificial Intelligence themes in Healthcare sectors must make sure that the information of the patient is secure. Balzano, a Switzerland-based startup that is building deep learning models for a geologist with the help of training data dot link to an open promisors server of Nvidia v100 tensor GPU. For developing advanced data sets for musculoskeletal orthopedics-related tools. The company has labeled more than a hundred radiology images each month and has adopted training data.in saving the company a lot of engineering efforts for building a similar solution from scratch.

Trainingdata.io also allows the startup to annotate and segment the features of the knee and cartilage more effectively. As they are ramping up their annotation process they are certain that the platform will empower them to leverage AI capabilities to their best and ensure the segmented images are of high quality.

• Viz.ai

Viz.ai is yet another robust Artificial Intelligence platform who are improving the Healthcare sector with their inclusion software system. One of the primary features of the software is the promise to reduce the time of the treatment, improve the access to care, and speed up the diffusion of medical innovation. They tend to accelerate the time-saving and increase the provider productivity and with more time the providers could treat additional patients or recharge effectively. Patients with multidisciplinary needs can coordinate and improve outcomes for patients. Viz.ai was founded by Dr. Chris Mansi, a neurosurgeon who was frustrated by the delays and processes in the medical industry to make the best use of Artificial Intelligence to transform the healthcare sector to work effectively.

Viz.ai specializes in using Artificial Intelligence to synchronize the stroke in the systems and reducing systematic delays which stand between the patient and the life-saving treatments. It is a remarkable way of making the best possible use of cutting-edge technology to transform stroke workflow and patient care. Please dot status products that detect an alert stroke to the teams to suspected large vessel occlusion stroke and complete CT perfusion studies in their network itself just within minutes. Later, the stroke team consults in real-time through a HIPAA-compliance mobile interface driving the treatment as fast as possible to save the patient’s life.

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