[{"data":1,"prerenderedAt":374},["ShallowReactive",2],{"slug-7-things-you-can-expect-from-a-i-in-healthcare":3},{"post":4,"relatedPosts":232,"relatedBooks":373},{"id":5,"date":6,"date_gmt":7,"guid":8,"modified":10,"modified_gmt":11,"slug":12,"status":13,"type":14,"link":15,"title":16,"content":18,"excerpt":21,"author":23,"featured_media":24,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":29,"categories":30,"tags":44,"project_category":67,"contact_email_category":75,"yst_prominent_words":76,"class_list":85,"better_featured_image":134,"acf":167,"yoast_meta":176,"_links":179},30701,"2020-10-21T10:00:00","2020-10-21T08:00:00",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=30701&_wpnonce=de965400f8&status=auto-draft&type=post","2020-10-27T12:20:04","2020-10-27T11:20:04","7-things-you-can-expect-from-a-i-in-healthcare","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002F7-things-you-can-expect-from-a-i-in-healthcare",{"rendered":17},"7 Things You Can Expect From A.I. In Healthcare",{"rendered":19,"protected":20},"\n\u003Cp>\u003Cem>Note: This is the first part of our series on what A.I. can and can’t do. Check our the \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002F7-things-you-cant-expect-from-a-i-in-healthcare\" target=\"_blank\" rel=\"noreferrer noopener\">second part here\u003C\u002Fa>!\u003C\u002Fem>\u003C\u002Fp>\n\n\n\n\u003Cp>Artificial Intelligence (A.I.) has for long been the subject of the fertile minds of science-fiction writers and movie directors. HAL 9000, Skynet and JARVIS are some of the many A.I. names sci-fi enthusiasts are familiar with. They streamline administrative tasks, entertain humans and, of course, become overlords threatening human existence. \u003C\u002Fp>\n\n\n\n\u003Cp>Now, thanks to technological progress, such A.I. are breaking out of the confines of movies and books and into healthcare. While they aren’t threatening our existence, they are helping in improving the medical field. From forecasting disease outbreaks to helping in new drug discovery, the potential of A.I. in healthcare is attracting \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F826993\u002Fhealth-ai-market-value-worldwide\u002F\" target=\"_blank\">massive investments\u003C\u002Fa> and increasing \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fnew-study-the-state-of-artificial-intelligence-based-fda-approved-medical-devices-and-algorithms-an-online-database\" target=\"_blank\">life science research\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-768x432.png\" alt=\"artificial intelligence and COVID\" class=\"wp-image-27609\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>However, healthcare A.I. is a relatively juvenile field and the technology’s potentials might not be so clear-cut. This is largely influenced by A.I.’s exaggerated depiction and hype in popular media or simply due to poor understanding of the technology. As such, a clear picture of where we are heading with A. I. in healthcare can prove to be practical for medical professionals and patients alike. \u003C\u002Fp>\n\n\n\n\u003Cp>That’s the aim of this series of articles about the potential of A.I. in healthcare in the next decade or so. In this first article, we summarise 7 things this technology can bring to the field. And we discuss 7 things you can’t expect in the next piece, along with recommendations to prepare for the age of A.I.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The 7 things you can definitely expect\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Smart algorithms can sift through large volumes of data more quickly than humans ever can and derive trends from these analyses. Many of the possibilities listed below are still experimental or implemented on a small scale. But it’s only a matter of time before they are refined and deployed for wider adoption.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>1. Better organised healthcare logistics\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Annually, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.wsj.com\u002Farticles\u002Fdoctor-visits-could-provide-relief-to-uber-and-lyft-11562756401\" target=\"_blank\">some 3.6 million U.S. patients\u003C\u002Fa> miss their doctor’s appointment as a result of poor transportation services. However, even those who make it to those appointments are met with the inevitable waiting times. 97% of the 5000+ patients involved in \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.softwareadvice.com\u002Fresources\u002Fhow-to-treat-patient-wait-time-woes\u002F\" target=\"_blank\">a Software Advice survey\u003C\u002Fa> reported feeling frustrated by wait times at the doctor’s office. It’s not hard to relate. But when it comes down to it, those are logistics issues that can be enhanced with better organisation.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F04\u002Ffuture-of-ambulance-and-uber-768x432.jpg\" alt=\"\" class=\"wp-image-17697\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F04\u002Ffuture-of-ambulance-and-uber-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F04\u002Ffuture-of-ambulance-and-uber-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F04\u002Ffuture-of-ambulance-and-uber-512x288.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F04\u002Ffuture-of-ambulance-and-uber-444x250.jpg 444w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F04\u002Ffuture-of-ambulance-and-uber.jpg 870w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>By integrating an A.I. assistant into the healthcare system, it could guide patients and optimise the time spent during their medical journey with \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fcan-we-eliminate-waiting-times-from-healthcare-forever\u002F\" target=\"_blank\">a Waze-like approach\u003C\u002Fa>. It can determine where the queue is shorter and which test will take less time to perform for each patient. By connecting with \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fride-hailing-platforms-solve-problems-transportation-healthcare\u002F\" target=\"_blank\">non-emergency medical transportation (NEMT) rides\u003C\u002Fa> offered by ride-hailing platforms like Uber and Lyft, the algorithm can suggest which healthcare facility will be more time-efficient to visit and direct patients there. In this way, the time spent by each patient is optimised while they have a better healthcare experience.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>2. Boosting drug design to a new level\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Estimates put the numbers at \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2452302X1600036X#:~:text=Although%20the%20drug%20development%20takes,daunting%20and%20difficult%20to%20navigate.\" target=\"_blank\">about 12 years\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.fiercebiotech.com\u002Fr-d\u002Faverage-cost-of-drug-r-d-try-2-9b-on-for-size\" target=\"_blank\">$2.9 billion\u003C\u002Fa> for an experimental drug to advance from concept to market. This takes into account the time and resources invested in finding suitable candidates, addressing unexpected side effects in clinical trials and the multiple trial-and-error sequences. But with A.I., these numbers can be significantly slashed.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F06\u002Fpicture_004-768x432.png\" alt=\"\" class=\"wp-image-28737\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F06\u002Fpicture_004-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F06\u002Fpicture_004-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F06\u002Fpicture_004-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F06\u002Fpicture_004-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F06\u002Fpicture_004.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>As a proof-of-concept, the A.I. pharma startup Insilico Medicine identified a potential new drug \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.technologyreview.com\u002F2019\u002F09\u002F03\u002F133175\u002Fan-ai-system-identified-a-potential-new-drug-in-just-46-days\u002F\" target=\"_blank\">in only 46 days\u003C\u002Fa>. Its software achieved this by analysing hordes of data which would take humans years to go through. During the Ebola epidemic in 2015, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.atomwise.com\u002F2015\u002F03\u002F24\u002Fnew-ebola-treatment-using-artificial-intelligence\u002F\" target=\"_blank\">Atomwise used its A.I. algorithm\u003C\u002Fa> to identify two drugs with significant potential to reduce Ebola infectivity. It accomplished this effort in less than a day. With the potential that A.I. holds in drug discovery, it’s no mere coincidence that \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fblog.benchsci.com\u002Fstartups-using-artificial-intelligence-in-drug-discovery\" target=\"_blank\">over 230 startups\u003C\u002Fa> are using the technology for this purpose.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>3. Improve working conditions for medical professionals while saving lives\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-could-solve-alarm-fatigue-in-hospitals\u002F\" target=\"_blank\">Alarm fatigue\u003C\u002Fa> is an endemic problem among healthcare workers. It refers to the point where they become desensitised to alarm signs due to being exposed to incessant beeping alerts throughout the day. Some experience up to \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC4206416\u002F\" target=\"_blank\">187 alarms per bed per day\u003C\u002Fa>; of which \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fnurse.org\u002Farticles\u002Falarm-fatigue-statistics-patient-safety\u002F\" target=\"_blank\">72% to 99%\u003C\u002Fa> are false alarms. With the medical staff overburdened as they are, alarm fatigue predisposes them to miss that small fraction of alerts that do require medical attention. A study put those so-called “alarm-related deaths” to \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fasa.scitation.org\u002Fdoi\u002Fabs\u002F10.1121\u002F1.4950561\" target=\"_blank\">about 200 per year\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-768x432.png\" alt=\"alarm fatigue\" class=\"wp-image-26271\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-512x288.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>How about hearing 99% fewer alarms but only hearing that 1% that are clinically actionable? Researchers developed an A.I. that does just that and published their findings \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.jmir.org\u002F2019\u002F11\u002Fe15406\" target=\"_blank\">in a paper\u003C\u002Fa>. Their automatic reasoning mechanism helped reduce notifications received by caregivers by up to 99.3%. Such a feature will greatly improve working conditions in hospitals so that the staff can focus on those cases that require attention. Given those advantages, it’s easy to see this feature getting implemented.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>4. Finding new associations between risks and diseases\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>With their ability to analyse information, recognise patterns and derive trends in ways that humans can’t, A. I.-based algorithms can surprise us with new associations in medicine. For example, Google researchers \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-curious-case-of-a-i-discovering-unusual-associations-in-medicine\u002F\" target=\"_blank\">fed retinal images to an A.I.\u003C\u002Fa> to identify long-term health dangers. After going through enough data, the algorithm taught itself what to look for in retinal images to detect those with signs of cardiovascular risks.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FAI-association-small-768x432.jpg\" alt=\"AI association\" class=\"wp-image-27237\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FAI-association-small-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FAI-association-small-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FAI-association-small-1536x864.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FAI-association-small.jpg 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>In another case, researchers at the University of California in San Francisco \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalxpress.com\u002Fnews\u002F2018-11-artificial-intelligence-alzheimer-years-diagnosis.html\" target=\"_blank\">trained an algorithm\u003C\u002Fa> to recognise metabolic patterns associated with Alzheimer’s disease from brain scans. In later tests, the A.I. detected the condition about six years before the final diagnosis, with 100% sensitivity.\u003C\u002Fp>\n\n\n\n\u003Cp>There have been several such instances of \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-curious-case-of-a-i-discovering-unusual-associations-in-medicine\u002F\" target=\"_blank\">A.I. discovering unusual associations in medicine\u003C\u002Fa>; and we will likely come across more as the technology gets more widely adopted.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>5. Ushering the new era of the art of medicine\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Many might think that A.I. will strip the art of medicine from healthcare practice by taking over the tasks that medical professionals have been traditionally handling. On the contrary, it will facilitate \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-and-the-art-of-medicine\u002F\" target=\"_blank\">the real era of the art of medicine\u003C\u002Fa>. Bureaucratic tasks and managing health IT and EHR systems are among the major reported causes of \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fphysician-burnout\u002F\" target=\"_blank\">physician burnout\u003C\u002Fa>. But these aren’t related to the practice of medicine. Such mundane administrative tasks can be automated with algorithms, which will free up valuable time; time that physicians can subsequently dedicate to their patients and elucidating medical conditions.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F083_the_art_of_medicine-768x432.png\" alt=\"art of medicine\" class=\"wp-image-23755\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F083_the_art_of_medicine-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F083_the_art_of_medicine-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F083_the_art_of_medicine-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F083_the_art_of_medicine-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F083_the_art_of_medicine.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>A.I. algorithms can further assist in decision-making to improve the accuracy of diagnoses. For instance, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.futurity.org\u002Fartificial-intelligence-breast-cancer-detection-2261322\u002F#:~:text=boosts%20breast%20cancer%20detection%20accuracy,-January%2022nd%2C%202020&text=An%20artificial%20intelligence%20tool%E2%80%94trained,analysis%2C%20a%20new%20study%20finds.\" target=\"_blank\">several\u003C\u002Fa> \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.healthcareitnews.com\u002Fnews\u002Fasia-pacific\u002Fai-helps-radiologists-improve-accuracy-breast-cancer-detection-lesser-recalls\" target=\"_blank\">studies\u003C\u002Fa> show that with the help of A.I., radiologists improve the accuracy of cancer detection from radiological scans. In future scenarios, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-and-the-art-of-medicine\u002F\" target=\"_blank\">medical A.I. trained via reinforcement learning\u003C\u002Fa> could discover treatments and cures for conditions when human medical professionals could not. Cracking the reasoning behind such unconventional and novel approaches will herald the true era of art in medicine.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>6. Help forecast future outbreaks and pandemics\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>You might be familiar with this story by now; before either the WHO or the CDC issued warnings about COVID-19’s spread, it was Bluedot, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fhow-digital-health-technology-can-help-manage-the-coronavirus-outbreak\" target=\"_blank\">an A.I. company that did so\u003C\u002Fa>. Their algorithm went through news reports, airline data, and reports of animal disease outbreaks to detect trends. These were then analysed by epidemiologists who then alerted the company’s clients.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"254\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002Fcovid-19-ai-768x254.jpg\" alt=\"\" class=\"wp-image-27593\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002Fcovid-19-ai-768x254.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002Fcovid-19-ai.jpg 870w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption>Source: https:\u002F\u002Fwww.coe.int\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>As the virus spread, other organisations employed similar solutions. Soon after it appeared, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.worldpop.org\u002Fevents\u002Fchina\" target=\"_blank\">researchers fed an algorithm\u003C\u002Fa> with anonymised air travel and smartphone movement data to explore how the disease could spread from Wuhan to other cities. Another team \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.medrxiv.org\u002Fcontent\u002F10.1101\u002F2020.01.30.20019844v4\" target=\"_blank\">used an A.I. to model COVID-19’s spread\u003C\u002Fa> from case reports, human movement and public health interventions. This helped show how travel restrictions limited the contagion’s spread.\u003C\u002Fp>\n\n\n\n\u003Cp>Given that \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-sober-state-of-artificial-intelligence-in-the-fight-against-covid-19\u002F\" target=\"_blank\">A.I.’s contribution\u003C\u002Fa> became evident during the current pandemic, authorities will likely invest more in such forecasting methods. This will give them better insight into forthcoming disease outbreaks and better prepare for any eventuality.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>7. To get unbeatable at specific, data-oriented tasks\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>When softwares like IBM’s DeepBlue or Google’s AlphaGo \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Falphago-artificial-intelligence-in-healthcare\u002F\" target=\"_blank\">beat world champions at games\u003C\u002Fa> such as chess or Go, it sends a strong message that algorithms will become unbeatable in specific, data-oriented tasks. This is what software described as \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fa-physicians-visual-guide-to-artificial-intelligence\" target=\"_blank\">Artificial Narrow Intelligence\u003C\u002Fa> (ANI) excel at.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-768x432.jpg\" alt=\"\" class=\"wp-image-28295\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-1536x864.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image.jpg 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>Such algorithms can analyse the ever-increasing volume of medical information and research data, which is humanly impossible to do. From the insights gained, we can better understand complex conditions like cancer, which researchers are constantly making new discoveries about.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>To be continued…\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>That’s a wrap for the first article in this series. We got acquainted with the possibilities healthcare A.I. holds. Despite these being manifold, there are also limitations to what A.I. can achieve in healthcare. That’s the subject of the second entry to this series. \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002F7-things-you-cant-expect-from-a-i-in-healthcare\" target=\"_blank\" rel=\"noreferrer noopener\">Be sure to check it out\u003C\u002Fa>!\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>Note: This is the first part of our series on what A.I. can and can’t do. Check our the second part here! Artificial Intelligence (A.I.) […]\u003C\u002Fp>\n",16,30779,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33,34,35,36,37,38,39,40,41,42,43],504,798,491,521,499,800,516,489,497,512,515,799,494,[45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66],6285,134,6287,222,6289,281,3085,771,6291,6293,1142,6295,1168,6297,1224,4247,821,6299,1438,1530,6091,6281,[68,69,70,71,72,73,74],947,948,949,950,951,952,953,[],[77,78,79,80,81,82,83,84],1683,1723,1739,3193,3195,3555,5489,1661,[86,14,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133],"post-30701","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-digital-health-research","category-empowered-patients","category-future-medicine","category-healthcare-design","category-healthcare-policy","category-medical-education","category-personalized-medicine","category-portable-diagnostics","category-robotics","category-science-fiction","category-security-privacy","category-telemedicine","tag-hal-9000","tag-ai","tag-skynet","tag-ebola","tag-nemt","tag-ibm","tag-covid19","tag-ani","tag-insilico-medicine","tag-atomwise","tag-life-sciences","tag-alarm-fatigue","tag-smart-algorithm","tag-ibm-deepblue","tag-physician-burnout","tag-pandemic","tag-a-i","tag-alphago","tag-outbreak","tag-drug-design","tag-google-a-i","tag-jarvis","project_category-company","project_category-developers","project_category-educators","project_category-medical-professionals","project_category-patients","project_category-policy-makers","project_category-researchers",{"id":24,"alt_text":135,"caption":27,"description":136,"media_type":137,"media_details":138,"post":5,"source_url":166},"things you can and can't expect from A.I.","Offering a clear picture of where we are heading with A. I. in healthcare, part two: 7 things you can not expect from artificial intelligence in healthcare.","image",{"width":139,"height":140,"file":141,"sizes":142,"image_meta":164},1920,1080,"2020\u002F10\u002F214_tmf-01.png",{"medium":143,"large":149,"thumbnail":154,"medium_large":158,"1536x1536":159},{"file":144,"width":145,"height":146,"mime-type":147,"source_url":148},"214_tmf-01-370x208.png","370","208","image\u002Fpng","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F214_tmf-01-370x208.png",{"file":150,"width":151,"height":152,"mime-type":147,"source_url":153},"214_tmf-01-768x432.png","768","432","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F214_tmf-01-768x432.png",{"file":155,"width":156,"height":156,"mime-type":147,"source_url":157},"214_tmf-01-150x150.png","150","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F214_tmf-01-150x150.png",{"file":150,"width":151,"height":152,"mime-type":147,"source_url":153},{"file":160,"width":161,"height":162,"mime-type":147,"source_url":163},"214_tmf-01-1536x864.png","1536","864","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F214_tmf-01-1536x864.png",{"aperture":165,"credit":27,"camera":27,"caption":27,"created_timestamp":165,"copyright":27,"focal_length":165,"iso":165,"shutter_speed":165,"title":27,"orientation":165},"0","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F214_tmf-01.png",{"cta_type":168,"cta_color":27,"subtitle":27,"related_books":169,"related_posts_footer":172,"related_posts":20},"subscribe",[170,171],24762,24763,[173,174,175],13650,10785,30149,{"yoast_wpseo_title":177,"yoast_wpseo_metadesc":178,"yoast_wpseo_canonical":15},"7 Things You Can Expect From A.I. In Healthcare - The Medical Futurist","Offering a clear picture on where we are heading with A. I. in healthcare, part one: 7 things you can expect from artificial intelligence in healthcare.",{"self":180,"collection":186,"about":189,"author":192,"replies":195,"version-history":198,"predecessor-version":202,"wp:featuredmedia":206,"wp:attachment":209,"wp:term":212,"curies":228},[181],{"href":182,"targetHints":183},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F30701",{"allow":184},[185],"GET",[187],{"href":188},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[190],{"href":191},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[193],{"embeddable":26,"href":194},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[196],{"embeddable":26,"href":197},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=30701",[199],{"count":200,"href":201},10,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F30701\u002Frevisions",[203],{"id":204,"href":205},30887,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F30701\u002Frevisions\u002F30887",[207],{"embeddable":26,"href":208},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F30779",[210],{"href":211},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=30701",[213,216,219,222,225],{"taxonomy":214,"embeddable":26,"href":215},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=30701",{"taxonomy":217,"embeddable":26,"href":218},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=30701",{"taxonomy":220,"embeddable":26,"href":221},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=30701",{"taxonomy":223,"embeddable":26,"href":224},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=30701",{"taxonomy":226,"embeddable":26,"href":227},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=30701",[229],{"name":230,"href":231,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[233],{"id":175,"date":234,"date_gmt":235,"guid":236,"modified":238,"modified_gmt":239,"slug":240,"status":13,"type":14,"link":241,"title":242,"content":244,"excerpt":246,"author":23,"featured_media":248,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":249,"categories":250,"tags":251,"project_category":272,"contact_email_category":273,"yst_prominent_words":274,"class_list":277,"better_featured_image":299,"acf":321,"yoast_meta":329,"_links":331},"2020-09-15T10:00:00","2020-09-15T08:00:00",{"rendered":237},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=30149&_wpnonce=9d9292e25f&status=auto-draft&type=post","2020-09-16T10:54:20","2020-09-16T08:54:20","new-study-the-state-of-artificial-intelligence-based-fda-approved-medical-devices-and-algorithms-an-online-database","https:\u002F\u002Fmedicalfuturist.com\u002Fnew-study-the-state-of-artificial-intelligence-based-fda-approved-medical-devices-and-algorithms-an-online-database",{"rendered":243},"New Study: The State Of A.I.-Based, FDA-approved Medical Devices And Algorithms – An Online Database",{"rendered":245,"protected":20},"\n\u003Cp>Regulatory authorities such as the U.S. Food & Drug Administration (FDA) and the European Medicine Agency (EMA) are heavily regulating the medical landscape when it comes to artificial intelligence (A.I.)-based solutions. The FDA, in particular, took the lead, having issued a specific framework for A.I.-based algorithms. \u003C\u002Fp>\n\n\n\n\u003Cp>However, even this leading authority, like other regulatory bodies, does \u003Cstrong>\u003Cem>not provide a comprehensive database\u003C\u002Fem>\u003C\u002Fstrong> of these tools that it has approved. One can expect such information to be readily available, especially for a prominent technology like A.I. But the very authorities regulating the landscape failed to provide it.\u003C\u002Fp>\n\n\n\n\u003Cp>It’s \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F826993\u002Fhealth-ai-market-value-worldwide\u002F\" target=\"_blank\">a booming market\u003C\u002Fa> and it’s not because of its hype. The number of life science studies published around A.I. rose from 596 in 2010 to 12,422 in 2019. Such smart algorithms promise to augment medical practice from \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-could-solve-alarm-fatigue-in-hospitals\u002F\" target=\"_blank\">eliminating alarm fatigue\u003C\u002Fa> through \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-prosthetics-depends-on-a-i\u002F\" target=\"_blank\">improving prosthetics\u003C\u002Fa> to \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-sober-state-of-artificial-intelligence-in-the-fight-against-covid-19\u002F\" target=\"_blank\">managing a pandemic\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>Moreover, according to forecasts, the global market size for A.I. in healthcare will soar past its $1 billion valuation in 2016 to \u003Ca href=\"https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F826993\u002Fhealth-ai-market-value-worldwide\u002F\">$28 billion in 2025\u003C\u002Fa>. This trend is not showing any signs of slowing down as we march steadily into the A.I. era of healthcare. As such, expectations are high within the medical community.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-768x432.png\" alt=\"artificial intelligence and COVID\" class=\"wp-image-27609\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F04\u002FAI-and-COVID-small.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">The TMFI study\u003C\u002Fh2>\n\n\n\n\u003Cp>Given the importance of A.I. in healthcare, companies have a tendency to overuse the term when describing their solutions. They label their devices or software as A.I.-based when in fact this is not the case. This is a common move to attract investors and to boost the company’s public image. But even major regulatory bodies have shown lacunae in making information on credible A.I.-based medical tools available.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>The latest peer-reviewed paper from \u003C\u002Fstrong>\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ftmfinstitute.org\u002F\" target=\"_blank\">\u003Cstrong>The Medical Futurist Institute\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong> (TMFI) analysed the state of regulation over A.I.-based algorithms. Using the FDA as an example, the authors even pioneered the first \u003C\u002Fstrong>\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffda-approved-ai-based-algorithms\u002F\" target=\"_blank\">\u003Cstrong>open access, online database\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong> of FDA-approved A.I.-based algorithms, which the U.S.-based regulatory body should have come up with already. \u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>The results of this study led by Dr. Meskó were recently published in npj Digital Medicine, the first TMFI study to be published in this prestigious journal. Here we break down the main findings, and you can also read the full, open-access paper on \u003C\u002Fstrong>\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41746-020-00324-0\" target=\"_blank\">\u003Cstrong>npj Digital Medicine\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong>.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>How the FDA regulates A.I.-based algorithms in healthcare\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>While novel, A.I.-based medical devices show promise in enhancing medicine, they are inevitably high-risk in nature. There are several unknown consequences of using A.I. in medical decision-making and data analysis. As such, the FDA imposes strict regulatory requirements to licence such medical devices.\u003C\u002Fp>\n\n\n\n\u003Cp>Prior to legally marketing their medical hardware or software in the U.S., the company must submit it to the FDA for evaluation. Medically-oriented A.I.-based algorithms have 3 levels of clearance and they must meet specific criteria requirements to be granted a clearance. These clearance levels are \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.fda.gov\u002Fmedical-devices\u002Fdevice-approvals-denials-and-clearances\u002F510k-clearances\" target=\"_blank\">510(k)\u003C\u002Fa>, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.fda.gov\u002Fmedical-devices\u002Fpremarket-submissions\u002Fpremarket-approval-pma\" target=\"_blank\">premarket approval\u003C\u002Fa> (PMA) and the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.fda.gov\u002Fmedical-devices\u002Fpremarket-submissions\u002Fde-novo-classification-request\" target=\"_blank\">de novo pathway\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-768x432.jpg\" alt=\"\" class=\"wp-image-28295\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image-1536x864.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F05\u002FAI-doctor-image.jpg 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>An algorithm gains 510(k) clearance if it is shown to be at least as safe and effective as another similar, legally-marketed algorithm. PMA is issued to algorithms for Class III medical devices, or those with a large impact on human health. Their safety is determined after confirming that their effectiveness is supported by satisfactory scientific evidence. As for the de novo pathway, it relates to novel medical devices for which there are no legal counterparts on the market. General controls offer reasonable guarantee of their safety and effectiveness.\u003C\u002Fp>\n\n\n\n\u003Cp>However, despite the FDA’s thorough regulatory processing, Meskó \u003Cem>et al. \u003C\u002Fem>found several issues with its method as well as with accessing relevant information to its approved medical A.I.-based tools.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Issues with the FDA’s regulation\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Even if it has strict requirements before a tool is given clearance, the FDA does not ask companies to categorise their technology as A.I.-based. Moreover, its official approval announcements do not clearly state the use of these methods either. This showed a blatant lack of clarity from the leading regulatory body itself regarding a prominent technology. Further clouding clarifications on such matters is the FDA’s official website. Its search engine does not have any feature allowing users to search for specific queries in FDA announcements and summaries. This severely hampers the accessibility of the database.\u003C\u002Fp>\n\n\n\n\u003Cp>Nevertheless, the FDA is not the only one with such issues. One can expect regulatory authorities to provide clear descriptions of the devices they regulate, and offer a properly searchable database to assess the implementation of new techniques. Unfortunately, no regulatory agencies offer these possibilities.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002Fradiologist_001-1-768x432.png\" alt=\"A.I. in medicine\" class=\"wp-image-24009\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002Fradiologist_001-1-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002Fradiologist_001-1-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002Fradiologist_001-1-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002Fradiologist_001-1-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002Fradiologist_001-1-512x288.png 512w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>With the FDA taken as an example given that it took the leadership in A.I.-based medical device regulation and has the necessary toolkit to assess the credibility of these tools, the purpose of the new paper was three-fold:\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>1. To provide an insight into the currently available A.I-based medical devices and algorithms approved by the FDA.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>2. To create an up-to-date database of FDA-approvals in this field which is open to submissions and might serve as the database that the FDA should have.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>3. To raise awareness of the importance of regulatory bodies clearly stating whether a medical device is A.I.-based or not.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Building the first open access database of FDA-approved A.I.-based algorithms\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>The research team, composed of \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fstanbenjamens\u002F\" target=\"_blank\">Dr. Stan Benjamens\u003C\u002Fa>, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fpranavsingh-dhunnoo-m-d-3a95a5148\u002F\" target=\"_blank\">Dr. Pranavsingh Dhunnoo\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fbertalanmesko\u002F\" target=\"_blank\">Dr. Bertalan Meskó\u003C\u002Fa>, took it upon themselves to gather all of the A. I.-based medical tools approved by the FDA into a comprehensive online database. Since the U.S.-based regulatory body does not require a clear definition for these solutions, the study authors agreed on one. They classify a technology as A.I.-based if its development includes a form of machine learning, a computer-based method to create algorithms based on structured databases. A more advanced subtype of machine learning, deep learning, a deep neural network (DNN)-based methodology, for pattern recognition, is also common in A.I.-based technologies. \u003C\u002Fp>\n\n\n\n\u003Cp>Since A.I.-based technologies in medicine started gaining traction in the mid-2010s, the authors combed FDA announcements between January 2010 and March 2020. They found 64 A.I.-based, FDA-approved medical devices and algorithms during this timeframe. Out of those, only 29 mentioned any A.I.-related expressions in the official FDA announcement. The remaining 35 were described as A.I.-based technologies on other online sources. \u003C\u002Fp>\n\n\n\n\u003Cp>From these results, Meskó \u003Cem>et al. \u003C\u002Fem>have built \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffda-approved-ai-based-algorithms\u002F\" target=\"_blank\">an online database\u003C\u002Fa> where the authors independently reviewed each entry. To help in visualising the results, they also created an infographic, which you can see below:\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002FFigure-2-768x432.png\" alt=\"\" class=\"wp-image-30151\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002FFigure-2-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002FFigure-2-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002FFigure-2-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002FFigure-2-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002FFigure-2.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>The infographic contains each approved device’s name, a short description, its relation to a primary and a secondary medical specialty and its type of FDA clearance. The same colors are assigned to the same medical specialty.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>An open invitation\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Creating \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffda-approved-ai-based-algorithms\u002F\" target=\"_blank\">the database\u003C\u002Fa> and raising awareness of its importance were only the beginning. It features a submission option where the community can submit FDA-cleared A.I.-based medical solutions that came out after the study’s completion or even if the authors missed any. The authors also cross-check and verify all submissions before making any additions.\u003C\u002Fp>\n\n\n\n\u003Cp>Moreover, the authors further encourage regulatory bodies to take over this database or launch their own since they possess the resources to better maintain such a tool. No such database exists other than the one created for this pioneering study. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>As A.I.-based devices and algorithms for medical purposes are becoming integral parts of the healthcare landscape, the importance of such an easily accessible and informative database will become more apparent. Regulatory authorities have the duty to maintain one to allow a better oversight of the credible A.I. tools managing our health.\u003C\u002Fstrong>\u003C\u002Fp>\n",{"rendered":247,"protected":20},"\u003Cp>Regulatory authorities such as the U.S. Food & Drug Administration (FDA) and the European Medicine Agency (EMA) are heavily regulating the medical landscape when it […]\u003C\u002Fp>\n",30249,{"_acf_changed":20,"footnotes":27},[31,32],[252,253,254,255,256,257,61,258,259,260,261,262,263,264,265,266,267,268,269,270,271],144,753,166,775,197,203,6173,224,823,6175,236,6177,307,6179,330,6181,463,137,658,1066,[68,69,71,73,74],[],[78,275,276],4505,5067,[278,14,87,88,89,90,91,92,93,279,280,281,282,283,284,121,285,286,287,288,289,290,291,292,293,294,295,296,297,298,127,128,130,132,133],"post-30149","tag-artificial-intelligence","tag-ophthalmology","tag-cardiology","tag-emergency-medicine","tag-database","tag-diabetes","tag-ct","tag-ecg","tag-cardiovascular","tag-blood-glucose","tag-fda-2","tag-neurology","tag-medical-diagnosis","tag-a-i-tools","tag-nature","tag-npj","tag-x-ray","tag-algorithm","tag-oncology","tag-mr",{"id":248,"alt_text":300,"caption":301,"description":302,"media_type":137,"media_details":303,"post":175,"source_url":320},"algorithms in medicine","The first online database of FDA-approved A.I.-based algorithms – 2020","The Medical Futurist Institute (TMFI) pioneered the first open-access, online database of FDA-approved A.I.-based algorithms.",{"width":139,"height":140,"file":304,"sizes":305,"image_meta":319},"2020\u002F09\u002F207_tmf-FDA-01.png",{"medium":306,"large":309,"thumbnail":312,"medium_large":315,"1536x1536":316},{"file":307,"width":145,"height":146,"mime-type":147,"source_url":308},"207_tmf-FDA-01-370x208.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002F207_tmf-FDA-01-370x208.png",{"file":310,"width":151,"height":152,"mime-type":147,"source_url":311},"207_tmf-FDA-01-768x432.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002F207_tmf-FDA-01-768x432.png",{"file":313,"width":156,"height":156,"mime-type":147,"source_url":314},"207_tmf-FDA-01-150x150.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002F207_tmf-FDA-01-150x150.png",{"file":310,"width":151,"height":152,"mime-type":147,"source_url":311},{"file":317,"width":161,"height":162,"mime-type":147,"source_url":318},"207_tmf-FDA-01-1536x864.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002F207_tmf-FDA-01-1536x864.png",{"aperture":165,"credit":27,"camera":27,"caption":27,"created_timestamp":165,"copyright":27,"focal_length":165,"iso":165,"shutter_speed":165,"title":27,"orientation":165},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F09\u002F207_tmf-FDA-01.png",{"cta_type":168,"cta_color":27,"subtitle":27,"related_books":322,"related_posts_footer":325,"related_posts":20},[323,324],24761,24764,[326,327,328],21320,24858,15375,{"yoast_wpseo_title":330,"yoast_wpseo_metadesc":302,"yoast_wpseo_canonical":241},"New Study: The State Of A.I.-Based, FDA-approved Medical Devices And Algorithms – An Online Database - The Medical Futurist",{"self":332,"collection":337,"about":339,"author":341,"replies":343,"version-history":346,"predecessor-version":350,"wp:featuredmedia":354,"wp:attachment":357,"wp:term":360,"curies":371},[333],{"href":334,"targetHints":335},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F30149",{"allow":336},[185],[338],{"href":188},[340],{"href":191},[342],{"embeddable":26,"href":194},[344],{"embeddable":26,"href":345},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=30149",[347],{"count":348,"href":349},19,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F30149\u002Frevisions",[351],{"id":352,"href":353},30231,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F30149\u002Frevisions\u002F30231",[355],{"embeddable":26,"href":356},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F30249",[358],{"href":359},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=30149",[361,363,365,367,369],{"taxonomy":214,"embeddable":26,"href":362},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=30149",{"taxonomy":217,"embeddable":26,"href":364},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=30149",{"taxonomy":220,"embeddable":26,"href":366},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=30149",{"taxonomy":223,"embeddable":26,"href":368},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=30149",{"taxonomy":226,"embeddable":26,"href":370},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=30149",[372],{"name":230,"href":231,"templated":26},[],1789237974444]