Healthcare is the hottest field in AI development


GLANCE

Based on CB Insights, AI startups in the healthcare sector have attracted more capital than other sectors. AI healthcare startups have raised $4.3B through 576 deals in 2013 and the rate of growth continues to rise in the two initial quarters of the year have seen the highest level of investment in the field to date.

Additionally, AI-powered apps that were developed by the top companies in the field of healthcare as well as this young generation of startups aren't just in the domain of R&D or research. These apps are starting to receive government approval to be utilized in doctors' offices, hospitals , and in homes across the globe.

Fast-Track Government Approval

In America The FDA has come out as an early supporter of AI applications in diagnostics and Medical Datasets imaging in light of recent developments in this area.

The FDA started an experiment in January of this year where healthcare software developers can be certified as "Pre certified". The idea behind this was that software can be easily modified to address glitches or other safety issues, so when a company can prove that they've developed reliable and reliable diagnostic tools based on software that are pre-certified, they will be able to facilitate a quicker speed of development. The FDA is now looking to broaden the scope of the initiative to incorporate AI software due to the potential of AI software.

This rapid-track regulatory approval could create a major opportunities for the more than 70 AI startups operating in the healthcare sector which have raised capital over the last 5 years.

Prior to the time this pre-certification programe is in place and in place, FDA has been approving various AI software applications. FDA has been approved for a range of AI software programs. In April this year, in April, the FDA granted approval to an AI software that has the ability to detect patients suffering from diabetic retinopathy. It is a study field which the Neuromation data scientists published previously on. This program was granted "breakthrough device designation" to expedite its approval and allow it to go on the market faster.

Other AI applications that have recently received approval include one developed by Viz.ai for the analysis of CT scans to detect possible stroke-related risk among patients. Startup Arterys received FDA approval for cloud-based AI platform that analyzes medical imaging of the cardiac system and in the same year, received approval for software that can automate detection of lung and liver lesion in cancer patients.

Another area of exciting innovation is AI-powered image analysis for consumers using smartphones. The FDA has recently approved a urine analysis at home kit by Dip.io that uses smartphones cameras to analyze the dipstick. Another startup Skin Vision makes use of smartphones to assess the health of skin at home.



Pattern detection

The pipeline of AI-powered diagnostic applications is quite extensive and if anything, we can expect an increase in the number of apps that are available in the near future.

AI has been proven to have the ability to discern the patterns of data in ways that could have previously been unrecognizable or, even if were recognized, they were difficult to be detected by medical professionals.

Many companies are making use of AI to create blood tests to detect various cancers. AI is also being utilized to analyze of risk factors that are secondary to illnesses, that were considered to be too obscure to use as a precise method of diagnosing. This includes AI applications that detect heart issues, age and smoking history through analyzing patterns in the retina or diagnosing coronary artery diseases by analyzing a patient's voice.

Pharmaceutical Research

Pharmaceutical industry top players Amgen, GlaxoSmithKline, Merck, Novartis, Pfizer and Sanofi are all looking to form alliances with AI startups in the field of automated discovery of drugs.

The methods utilized in these partnerships include modeling of healthy and diseased human cells for an analysis of the impact of potential drugs on an extent that was previously unattainable. Another option is to make use of billions of pieces of unstructured , isolated information from research papers, clinical trials, patents, or patient files to uncover patterns that were not previously noticed that could result in new drugs. Other researchers, such as Neuromation is using GANs that are generative (GAN's) to develop completely new molecules and determine the potential of their activity. These techniques could lead quicker, less expensive and more efficient drug discovery

Neuromation Activity in Healthcare

Neuromation is active in Healthcare research and has published five scientific papers in journals of repute on the use of artificial intelligence in the field of medicine imaging as well as drug development. These reports include research using deep learning to identify diabetic retinopathy (a most frequent causes of blindness) as well as breast cancer image analysis, and the detection of angiodysplasia (a common cause of intestinal bleeding as well as anemia).

Neuromation collaborates with health industry professionals to create facial recognition and image recognition tools for early detection of disease and diagnosis. Neuromation is a pioneer in the development of synthetic data that can be used to identify rare diseases and difficult to catalog for the reasons that data sets might not exist.

In the field of Pharmaceutical research and discovery of drugs, this year Neuromation released a paper about 3D Molecular Representations in the journal Molecular Pharmaceutics in conjunction with the partner Insilco Medicine.

Additionally, Neuromation provides services in the field of development and training of machine-learning models that generate and classify molecules likely to exhibit desirable characteristics for molecular biology as well as application of drug detection.



WHAT GTS OFFER

At Global Technology Solutions, we create Premium AI Training Datasets and Data Quality Management  to provide the needed support for medical data sets. GTS offers a wide variety of services that comprise annotation, tuberculosis x ray dataset, data collection, and protected transcription in order to provide support for your healthcare datasets for machine learning.

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