6th World Machine Learning and Deep Learning Congress

Technology presently has the ability to bring the promise of genomics and precision medicine directly into the clinical work process, while establishing a foundation for trial and research. This enables the business to apply new genomic data models in a sensible way — to deliver the right information to the supplier at the opportune time, while creating "research-ready" data to support a variety of objectives. This can transform the way we care for a wide range of diseases and conditions – from cancer to hyperlipidemia to diabetes to renal disease to neurodevelopmental disorders. The ultimate goal is to drive better and more accurate diagnoses, treatments and outcomes — while simultaneously making this knowledge available for research and pharmacogenomics. Machine learning is additionally engaging us to examine patient data at a level never possible. We can now transform data into insights and significant data. Simply think how a "data lake,"

As we live in the new world of quality, value-based care, we must be able to draw more insights and conclusions from ever-increasing amounts of information. We have the data, now we must put it to work. When we combine all of this data with machine learning , we are equipped to make smarter decisions. We have the ability to change healthcare – from the way we utilize electronic health records to the way we anticipate and deliver care. Most Electronic Health Records are based on innovation that is 20 or 30 years of age. For the most part, EHRs have stayed aware of rapid changes in healthcare by making incremental improvements over time. Be that as it may, it is trying to retrofit EHRs to take full advantage of new technologies. EHRs must accomplish more than data storage. They ought to be smart enough to convey the right data at the perfect time, at the purpose of care. When an EHR is controlled by machine learning, it can pre-populate data based on usage patterns and c

In my earlier blog I wrote about the how Artificial Intelligence , Machine Learning & Deep Learning are changing the world of Medical Science by contributing in the Medical Imaging, Doctor’s Report & Test Results. We also talked about the HealthCare areas where AI is being used to transform the lives such as Tumor Detection, Tracking Tumor Development, Blood Flow Quantification and Visualization, Medical Interpretation, Diabetic Retinopathy, digital X-ray imaging, Neuroscience, Oncology, Brachytherapy, Bone health, Prenatal health, Gynecological health, Orthopedics, Osteoporosis, Rheumatology etc. In this blog, we will see who the market’s big players are worldwide adapting the new technologies to enhance the machines used to enhance the Medical Science experience.           Fujifilm Holdings;           Ge Healthcare           Siemens Healthcare           Philips Healthcare           Shimadzu Corporation           Toshiba Medical Systems Corporation

What is Augmented Reality? The world Augmented Reality is a combination of two words, Augmented and Reality. The word Augmented has originated from another word Augment, which means to enhance something or to add. Augmented Reality which is also called AR is used to enhance user experience by adding graphics, sounds, and touch feedback into the natural world. It is also known as the "mixed reality" where, the technology overlays new information like any object. It may be window providing information of climate, news, motion picture or super cars on top of the existing environment. One of the best example of this is, Pokémon Game; hope you played that game. That’s Augmented Reality!!! looks like, in the same environment Pokémon comes around and the gamer tries to catch it by the Pokeball. The Virtual reality is slightly different. It is a  computer-generated simulation  or replacement of one’s environment for example when you put on VR Headset it complete