How Artificial Intelligence(AI) – Machine Learning is impacting our lives ?

Artificial Intelligence

Artificial Intelligence (AI) -Machine Learning in the past two decades the has done rapid advancement and its implementation changing the way we work & live.

We will read in detail How Artificial Intelligence(AI)-Machine Learning is impacting our lives ?

According to a Forbes report, healthcare, advanced nursing, and mental health diagnosis and treatment belong to several industries where AI is expected to “revolutionize.” These predictions are provided by a panel of Forbes technical committee members that ranks 13 industries that will quickly benefit from AI and machine learning technology.

Although the healthcare field has only just begun its Artificial Intelligence journey, the report points out that the technology shows great hope in many areas such as drug safety and imaging.

Ciox Health chief digital officer Florian Quarre said in the report: “Once the point of true information interoperability is reached and the safe exchange of health data is supported, all these commitments will work together to bring breakthroughs to patients.”

In addition, the report also recommends that artificial intelligence can provide resources to make older people feel confident about living alone and provide support for caregivers. It also pointed out that artificial intelligence can be used to identify young people’s mental health problems.

“We are beginning to see increased mental health problems among young people. Whether it is device addiction or withdrawal from the physical world, some people are starting to isolate themselves online,” retired technical director Chris Kirby reported Said. “This will eventually lead to the collapse of social cohesion. I discovered the potential of using AI to identify potential dangers and recommend treatment before they fall into the trap of depression and despair.

Report says the user experience of chatbot applications needs improvement

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  • 2020-05-09 14:47:07
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According to the latest survey from San Francisco-based human insight platform UserTesting, although more and more healthcare providers are using chatbots (or conversational AI) to establish contact with patients, the technology needs to be improved in certain areas to achieve broadly used.

That is, chatbots can improve customer service capabilities, handle complex conditions and earn users’ trust.

“Our research shows the importance of using human insight to understand consumer emotions and preferences to create a superior customer experience-it can ultimately determine the success or failure of next-generation applications such as chatbots and conversational AI,” Janelle Estes Chief Insights UserTesting Of officials said in a statement.

The results of the survey are based on a competitive benchmark study of five top medical chatbot applications, which are Ada, HealthTap, Medidict, Your.MD, and Symptomate. The report focused on feedback from 500 participants who evaluated each chatbot application according to the following criteria: ease of use, speed, reputation, beauty, and pleasure.

In terms of earning user trust, some people are concerned about HIPAA ’s compliance and standards, while other survey respondents want more background information about the application, such as comments and other historical records.

Regarding credibility, users will still say trustworthy questions:

Ada gets 83% reputation score from users

Mediktor wins 69%

Your.MD gets 47%

HealthTap wins 46%

People with symptoms earn 41%

About complex diagnosis:

Nearly 74% of Mediktor and Ada users are satisfied with the app’s ability to diagnose complex conditions

65% of Your.MD users recognize their ability to diagnose complex diseases

About 46% of Symptomate users and 43% of HealthTap users are confident in the app’s ability to diagnose complex conditions

None of the apps performed well in the “Happy” category, but 73% of users found chatbots helpful. Twelve percent think these apps are not helpful, and 15 percent are neutral.

The report says: “The healthcare industry is on the verge of change, and consumers and providers expect solutions to meet their changing needs and high expectations.” “These applications and other applications in their space are ideally suited Immediately start injecting customer insight into every decision they make. The difference between a winner and a loser will be in dependence on human insight. “

HHS technical challenges use AI to create digital solutions

The agency announced that the sprint technology challenge led by HHS has been completed to create new data-driven digital tools that benefit the public.

The 14-week technical challenge, the Opportunity Program (TOP) Health, involves 11 participating teams, using federal data and AI technology to develop digital tools to improve clinical trials, experimental therapies, and data-driven solutions such as cancer Lime complex challenges the disease. The HHS Office of the Chief Technology Office and the President’s Innovation Researcher conducted the first ever challenge.

Ed Simcox, chief technology officer of HHS, said in a statement: “At HHS, we recognize that only the federal government can solve our most important and complex challenges.” Industry skills and public resources are important steps to promote better health outcomes. “

Participating teams can access HHS selected data sets and address one of the two challenges in the technical sprint:

Artificial intelligence (AI), a method used to promote experimental therapeutic ecosystems.

Leverage the power of collaboration, citizen science and Lyme disease data.

After the challenge last week, the international team unveiled their digital tools, which will be maintained by industry and non-profit sponsors.

The following are the digital tools that appeared in the project:

The Philips Research Center in the Netherlands created Trial Explorer, which found the most suitable trial for patients.

Microsoft Healthcare in Israel created Microsoft Healthcare Bot, which uses conversational AI, advanced machine reading, and natural language processing to help patients and doctors find the appropriate clinical trials more efficiently.

A team with the Oak Ridge National Laboratory in Tennessee is planning to build a large knowledge graph representation for cancer clinical trials, which will make it possible to discover new concepts from unstructured text.

The Oracle TOP health team in Washington, DC has created a tool that uses AI to match patients fighting cancer to clinical trial plans.

TrialX in New York has developed iConnect, a patient recruitment tool that uses AI technology based on advanced semantics and decision engines to connect patients with clinical researchers.

A team at Flatiron Health in New York has developed a framework that improves the matching of patient trials and provides new treatments for oncology practices and their patients.

A team with Chicago ’s AheadIntoFun developed a platform for finding and sharing clinical trials.

A team of TrialX and the New York Global Lyme Alliance developed The Lyme Tracker App, a program that allows users to track symptoms to better understand the disease and its progress.

LivLyme Foundation created the TickTracker application, which allows users to track and report ticks in real time.

Another team at the LivLyme Foundation has created the TickTockBOOM application, which is a game-based application created by 11-year-old twins, designed to teach users about lice awareness and prevention methods.

A team at the California Functional Medicine Center has developed the Clyme Health app, which optimizes data collection and visualization for patients with complex, chronic diseases to help care providers and researchers better measure treatment effectiveness and use personality Data-driven solutions for chemical medicine identification.

These teams plan to show their tools in a future Washington DC show until the government shuts down.

Machine learning methods can improve treatment of ICU patients

Researchers at Princeton University have developed a machine learning method that can reduce the number of examinations for patients in the intensive care unit and shorten the time for critical treatment. And this method will soon enter ICU care.

This research was co-authored by Princeton University graduate student Cheng Lifang and Niragani Prasad. The study shows that this method can help clinicians intervene more quickly when the patient’s condition begins to deteriorate.

“A data-driven approach (for example, a method proposed by Cheng and co-authors, combined with a deeper understanding of clinical workflow, has the potential to reduce chart burden and the cost of over-testing, and improve situational awareness and results,” Shamim Nemati) Emory University (Emory University) biomedical informatics assistant professor, did not participate in the research of the doctor said.

The study was recently presented at the Pacific Biocomputing Symposium held in Hawaii on January 6. The study focused on four types of blood tests: lactic acid, creatinine, blood urea nitrogen, and white blood cell tests, all of which are used to diagnose kidney failure or sepsis in ICU patients. The researchers used data sets of more than 6,000 ICU patients.

The researchers ’algorithm uses a method called reinforcement learning, which encourages the test sequence based on the amount of information at a given time, which means that if the likelihood of a patient ’s state being significantly different is higher, there is a“ reward function ”to manage the test. The test starts. If the test results are likely to recommend clinical interventions to patients, then greater returns can also be obtained.

However, this method can also adversely affect the monetary cost of testing and patient risk. This method treats medical testing problems like a sequential decision. In this process, you have to consider all the decisions and all states you have seen in the past period of time, and decide what should be done at the current time to maximize the effect. Bring long-term returns for patients. “Prasad said.

The researchers found that this method can provide more information than the actual test protocol that clinicians usually follow. It also shows that the algorithm can reduce the number of laboratory tests ordered for white blood cell testing by as much as 44%.

The researchers plan to collaborate with data scientists from Penn Medicine’s Predictive Healthcare team to introduce this method into the clinic.

“This is the first time we can adopt this machine learning method and actually use it in an ICU or in-hospital, and provide advice to caregivers so that patients are not at risk”, Barbara Engelhardt, associate professor of computer science at Princeton University Said in a statement. “That’s really something new.”

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