{"id":1067836,"date":"2024-03-02T02:38:55","date_gmt":"2024-03-02T07:38:55","guid":{"rendered":"https:\/\/www.immortalitymedicine.tv\/ai-shows-promise-but-remains-limited-for-heart-stroke-care-idaho-business-review\/"},"modified":"2024-08-18T11:39:49","modified_gmt":"2024-08-18T15:39:49","slug":"ai-shows-promise-but-remains-limited-for-heart-stroke-care-idaho-business-review","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/ai-shows-promise-but-remains-limited-for-heart-stroke-care-idaho-business-review.php","title":{"rendered":"AI shows promise but remains limited for heart\/stroke care &#8211; Idaho Business Review"},"content":{"rendered":"<p><p>    Artificial    intelligence has the potential to change many aspects of    cardiovascular care, but not right away, a new report says.  <\/p>\n<p>    Existing AI and machine-learning digital tools are promising,    according to the scientific statement from the American Heart    Association. Such tools already have shown they can help screen    patients and guide researchers in developing new treatments.    The report was published Wednesday in the journal Circulation.  <\/p>\n<p>    But, the authors said, research hasnt shown that AI-based    tools improve care enough to justify their widespread use.  <\/p>\n<p>    There is an urgent need to develop programs that will    accelerate the education of the science behind AI\/machine    learning tools, thus accelerating the adoption and creation of    manageable, cost-effective, automated processes, Dr. Antonis    Armoundas, who led the statement writing committee, said in a    news release. He is a principal investigator at the Cardiovascular Research Center at Bostons    Massachusetts General Hospital.  <\/p>\n<p>    We need more AI\/machine learning-based precision medicine    tools to help address core unmet needs in medicine that can    subsequently be tested in robust clinical trials, said    Armoundas, who also is an associate professor of medicine at    Harvard Medical School.  <\/p>\n<p>    The report is the AHAs first scientific statement on    artificial intelligence. It looks at the state of research into    AI and machine learning in cardiovascular medicine and suggests    what may be needed for safe, effective widescale use.  <\/p>\n<p>    Here, we present the state of the art, including the latest    science regarding specific AI uses  from imaging and wearables    to electrocardiography and genetics, Armoundas said.  <\/p>\n<p>    AI can analyze data and make predictions, typically for    narrowly defined tasks. Machine learning uses mathematical    models and algorithms to detect patterns in large datasets that    may not be evident to human observers alone. Deep learning, a    subfield of machine learning, is used in image recognition and    interpretation.  <\/p>\n<p>    Researchers have used such technologies to analyze electronic    health records to compare the effectiveness of tests and    treatments, and, more recently, to create models that inform    care decisions.  <\/p>\n<p>    The report notes several ways digital tools might help    cardiovascular patients.  <\/p>\n<p>    Imaging, for example, is important for diagnosing heart attacks    and strokes. AI and machine-learning tools could address    inconsistencies in human interpretation and relieve    overburdened experts.  <\/p>\n<p>    AI has helped automate analysis of electrocardiograms, which    measure the hearts electrical activity, by identifying subtle    results that human experts might not see.  <\/p>\n<p>    And with implantable and wearable technologies providing steady    streams of health information, AI could help remotely monitor    patients and spot when something is amiss.  <\/p>\n<p>    But the report also spells out many challenges and limits.  <\/p>\n<p>    With imaging, for example, broad use of AI and machine learning    for interpreting tests is challenging because the data    available to study is limited. Researchers also need to prove    AI technology works in each area where it will be used.  <\/p>\n<p>    With implantable and wearable tech, the research gaps include    ways to identify which patients and conditions may be best for    AI- and machine learning-enabled remote monitoring. Other    issues include how to address cost-effectiveness, privacy,    safety and equitable access.  <\/p>\n<p>    More broadly, protocols on how information is organized and    shared are critical, the report says, and potential ethical,    legal and regulatory issues need to be addressed.  <\/p>\n<p>    And while AI algorithms have enhanced the ability to interpret    genetic variants and abnormalities, the writing committee    warned of limits. Such algorithms, the committee wrote, still    require training on human-derived data that can be error-prone    and inaccurate.  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Excerpt from:<br \/>\n<a target=\"_blank\" href=\"https:\/\/idahobusinessreview.com\/2024\/02\/28\/ai-shows-promise-but-remains-limited-for-heart-and-stroke-care\" title=\"AI shows promise but remains limited for heart\/stroke care - Idaho Business Review\" rel=\"noopener\">AI shows promise but remains limited for heart\/stroke care - Idaho Business Review<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> Artificial intelligence has the potential to change many aspects of cardiovascular care, but not right away, a new report says. Existing AI and machine-learning digital tools are promising, according to the scientific statement from the American Heart Association.  <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/ai-shows-promise-but-remains-limited-for-heart-stroke-care-idaho-business-review.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[1231415],"tags":[],"class_list":["post-1067836","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"modified_by":null,"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1067836"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=1067836"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1067836\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=1067836"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=1067836"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=1067836"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}