{"id":485,"date":"2019-04-08T23:57:55","date_gmt":"2019-04-08T12:57:55","guid":{"rendered":"https:\/\/spectdata.com\/?p=485"},"modified":"2020-12-15T22:22:42","modified_gmt":"2020-12-15T11:22:42","slug":"weekly-news-review-3","status":"publish","type":"post","link":"https:\/\/spectdata.com\/index.php\/2019\/04\/08\/weekly-news-review-3\/","title":{"rendered":"Weekly News Review – 8 Apr 2019"},"content":{"rendered":"\n

A weekly review of news articles regarding the application of AI in Healthcare by SpectData<\/em><\/p>\n\n\n\n

8 Apr 2019<\/h4>\n\n\n\n

AI has stepped into the field of marketing with a new perspective in life. They generate over one trillion data is collected by large scale computing power. This vital information is used to improve the discovery of new treatments and helps to assist patients outside the office.
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They reduced the cost of medicine and cutoff time required to analyze the records of patients and money. Using machine learning and deep learning we can understand the structure of an array board of technologies.
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Advancement in hardware and software helps to collect and analyze available data R&D companies invest 60% in almost any other sectors. These spend 50% of investments in clinical studies. AI begins to reverse the trends and leveraging past screen results. This helps the companies to develop drugs.
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Poor quality of data will not yield any analytical methods and meaningful insights. Data collected from the selected population will be lacking and meaningful. AI will raise the profit pools in the medical fields. Change of application will reduce the cost within a sector and add value within a sector. AI shifts yield within one healthcare sectors will threaten revenue over other sectors.
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Algorithms should be transparent and be credible.Machine learning does not provide any insights into data. This becomes witchcraft that people cannot understand.
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AI applications within a sector cause value to flow from a sector to either technological companies or consumers. This fields contains bio pharma, providers, payers, Med Tech, consumers and tech companies.
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AI includes Computer visions, voice recognition, NLP are used to give support for standard tasks. Example chatbots can be used to answer questions related to password resets and bills.
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Algorithms should be fair and measure the impact on society how they change the world. Using specific data may come with important trade-offs. To reduce risk machine learning will need to recognize the data context and protect key social values.
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The journey to integrate AI into strategies and operations must be a sustained one. But even companies that have yet to invest in AI can make some smart, low-risk moves to either enhance the positive value shifts or cut the negative impacts.<\/p>\n\n\n\n

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The page you are looking for cannot be found or is no longer available. If you think you have found this message in error, please contact us. Visit our homepage. Explore our most recent featured publications. Learn more about career opportunities at BCG.<\/p>\n\t\t<\/a>\n\t<\/div>\n\t

\n\t\t\"BCG\t\tBCG Global\t<\/div>\n<\/div><\/p>\n\n\n\n

AI has shaped the world and ultimately lead the world to the path of innovation.AI is used to give remote service to the early diagnosis of disease. For example, Biopharma is using AI to improve R&D and to an identification of better drug targets.
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AI uses speech and image recognition, machine learning, natural language, etc to gather data. These data are analyzed to data gather by them. These are the array board of technologies. It should not be confused with traditional business intelligence.
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AI is used in healthcare to reduce cost, to harvest the available data and provide the best healthcare possible<\/p>\n\n\n\n

AI is used to diagnosis the medical imaging and other clinical tests. This helps to find breast cancer, brain injury or heart diseases, etc. This saves a lot of time and money. These retinal images are used to give accurate outcomes and can cut half the price of treatment.
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Healthcare professional tools are used to create a plan for individual treatments. This cuts cost, reducing costs and improving outcomes. According to research, 2.8 million dollars are used on AI tools.
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R&D companies invest 60% in almost any other sectors. These spend 50% of investments in clinical studies. AI begins to reverse the trends and leveraging past screen results. This helps the companies to develop drugs.
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Now AI is used in the field of medicinal field, AI process many medical records and health care clinical records. Doctors used to spend one-third of the time to handle paper works and payers have to spend hours on spending administrative claims.
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Six levers can help drive better outcomes when harnessing machine learning in healthcare and pharmaceuticals.<\/p>\n\t\t<\/a>\n\t<\/div>\n\t

\n\t\t\"McKinsey\t\tMcKinsey & Company\t<\/div>\n<\/div> <\/p>\n\n\n\n

US healthcare generates approximately one trillion gigabytes of data. These immersive data is collected using large scale computing power. Using machine learning and artificial intelligence,  this vital information is used to improve the discovery of new therapeutics and to make the delivery of current ones more effective.
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The full potential of Machine learning requires recognizing and addressing issues raised to date. We can use machine learning to innovate and improving health.
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Using machine learning and artificial intelligence in marketing is different from using it in healthcare especially understanding of diseases and treatments. The most common health conditions remain understood.\u201dHalf of what we teach is wrong; we don’t know which half is true\u201d. Said by a dean.<\/p>\n\n\n\n

Statistical techniques pave the path to great findings. Poor quality data will not yield meaningful insights and analytical methods. Data-driven from selected populations will be lacking, but also generating incorrect conclusions.<\/p>\n\n\n\n

Machine learning algorithms are better than a conventional statistical approach with the data set to develop them. Conventional statistical approach faces limitation. Algorithms are informative and likely to remain additional insights.
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Machine learning does not provide information on how the insights of the data are provided. They leave a blank space. This becomes a form of witchcraft where the user cannot understand what the algorithm is doing.
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Machine learning algorithms offer a deep understanding of scientific and clinical consensus. The pharmaceutical manufacturer told that machine learning found that reducing low-density lipoprotein cholesterol after a heart attack is not associated with cardiac outcomes.
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A weekly review of news articles regarding the application of AI in Healthcare by SpectData 8 Apr 2019 AI has stepped into the field of marketing with a new perspective in life. They generate over one trillion data is collected by large scale computing power. This vital information is used to improve the discovery of … Continue reading Weekly News Review – 8 Apr 2019<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":355,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[6],"tags":[],"class_list":["post-485","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-highlights"],"jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p9PSm6-7P","jetpack_featured_media_url":"https:\/\/spectdata.com\/wp-content\/uploads\/2018\/11\/romain-vignes-53940-unsplash.jpg","_links":{"self":[{"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/posts\/485","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/comments?post=485"}],"version-history":[{"count":5,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/posts\/485\/revisions"}],"predecessor-version":[{"id":499,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/posts\/485\/revisions\/499"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/media\/355"}],"wp:attachment":[{"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/media?parent=485"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/categories?post=485"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/spectdata.com\/index.php\/wp-json\/wp\/v2\/tags?post=485"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}