Top 5 Ted Talk on How Machine Learning in Medical Field helping Human Race

Machine Learning in Medical Field helping human Race

Top 5 Ted Talk on How Machine Learning in Medical Field helping Human Race

The Future technologies and innovation is found to be the new ballgame for Healthcare. It’s going to bring adequacy and stability in the Healthcare sector.

The additional factors are the Reduced Errors, Complete Accuracy in the result of the Data Collected. The Artificial Intelligence, which automates the workflow of the Data. This helps to cut down the system cost and minimize the errors. This helps in Enhancing the Patients Experience.

Harnessing the Power of Artificial Intelligence to Diagnose Diseases by Kavya Kopparapu

Countless individuals reside in rural or developing regions where disease treatment is not the problem: it’s overcoming the 1:1,000 physician to patient ratio and screening for preventable diseases. High school student Kavya Kopparapu shares the near future of artificial intelligence in this area– as a replacement for doctors in these regions to provide a much-needed medical diagnosis.

Kavya Kopparapu is the Founder and CEO of both GirlsComputingLeague and current junior at Thomas Jefferson High School for Science and Technology. She is devoted to sharing her passion for computer science with others, especially young girls, since this area has given her a huge chance, also has been recognized by organizations such as the White House and the National Center for Women in Information Technology (NCWIT).

Her journey with computer science started in elementary school when she was introduced to the Scratch programming language and developed robots with the Mindstorms programming language. Her interests were fortified when she took AP Computer Science in freshman year, followed by classes like Artificial Intelligence and Computer Vision.

Artificial Intelligence in Healthcare – It’s about Time by Casey Bennett

We are in need of tools that may help us both clinicians and individuals create better health care decisions. Yet to be able to accomplish this, those tools will need to “feel as we do” and also the secret to natural intelligence isn’t simply X greater than Y, but instead sequences of choices over time. To help us in the best way, our medical computing tools must follow the approximate and the exact procedure. This kind of approach ties to prospective improvements across the wider healthcare area: cognitive computing, smart houses, cyborg clinicians, and robotics. He obtained his Ph.D. in the School of Informatics and Computing at Indiana University. His work concentrates on artificial intelligence in health care, such as the areas of robotics, machine learning, clinical decision support, and personalized medicine.

He had been the lead writer for Centerstone’s award-winning organization- the wide analytics platform (2010 TDWI Best Practices Award) and also the federal Knowledge Network Data Warehouse, the biggest ongoing clinical psychological health information repository from the nation. His work has also been featured as a member of IBM’s “Smarter Planet” effort. He’s now working on projects utilizing artificial intelligence to augment clinical decision-making from chronic illness, in addition to utilizing in-home robots for curative purposes with older individuals.

Better Medicine through Machine Learning by Suchi Saria

Faster medical treatment saves lives. Machine Learning is already saving lives, by scouring a large number of patients’ information, examining and comparing them to 1 patient’s health information to discover symptoms twelve to twenty-four hours prior to a doctor might.

“In several pressing medical issues, the answers to knowing whom to treat, when to treat, and how to treat with, would possibly already be in your data,” says Suchi Saria. find out how pants TREWS (Targeted Targeted Real-Time Early Warning Score) is really grateful for saving lives. Suchi Saria may be an academically the best Professor of technology excelling in Computer Science and health policy. She is the director of the Machine Learning and Health science lab at Johns Hopkins University. Her analysis is targeted on coming up with information solutions for providing personalized care.

Can AI accelerate a medical breakthrough?

While Jonathan Rezek was diagnosed with Parkinson’s disease, he discovered that there was no cure but just simply symptom management, assisted primarily by a fifty-year-old drug. Jonathan, a sales govt, dessert apple had an associate idea: what if Watson, IBM’s computing platform, might accelerate pharmaceutical research? With Watson’s facilitate a team semiconductor diode by associate IB, the man of science. Jonathan’s doctors are operating to revolutionize Parkinson’s analysis within the hopes of finding a breakthrough in the treatment.

About the TED Institute: We all know that innovative concepts and contemporary approaches to difficult issues will be discovered within visionary corporations around the world. Well. the TED Institute helps surface and shares these insights. Every year, TED works with a selected group of  Companies corporations and foundations to spot internal ideators, inventors, connectors, and creators. Drawing on an equivalent rigorous program that has ready speakers for TED’s main stage, TED Institute works closely with every partner, overseeing curation and providing intensive one-on-one speak development to sharpen and fine-tune concepts.

A.I. vs. Pathologists: Survival of the Fittest by Sahir Ali

Artificial Intelligence and its promise in predicting cancer outcome: each patient deserves their own equation. Dr. Sahirzeeshan Ali is known as an analysis

soul, the Research Scientist at the Centre of Computation Imaging and personalized medicine (CCIPD) at Case Western Reserve Medical University and Seidman Cancer Center. Dr. Ali received a bachelor’s and master’s degrees in Electrical and laptop engineering from Rutgers University (2009 & 2011) and a Ph.D. in medical engineering from Case Western Reserve University. He conjointly was the recipient of a Prostate Cancer Research Grant from the Department of Defense in 2014. Dr. Ali’s analysis and Research interest lie in developing image analysis, applied mathematics pattern recognition, machine learning and computer science tools to computationally interrogate medicine image information of digital pathology tissue pictures.

The tools were designed to predict the disease progression and supply a score to clinicians on the aggressiveness of a patient’s disease, like breast carcinoma and prostate cancer. This might successively facilitate physicians pick in deciding upon the most acceptable treatment possibility. Dr. Ali has written over thirty peer-reviewed journals, conference and abstract publications, showing in journals like Nature Scientific Reports, American Journal of Surgical Pathology, the Annual Review of medicine Engineering, Medical Image Analysis, IEEE Transactions on Medical Imaging. This analysis work has conjointly culminated in varied commercial patents.

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