[{"data":1,"prerenderedAt":445},["ShallowReactive",2],{"slug-simple-algorithms-vs-a-i":3},{"post":4,"relatedPosts":187,"relatedBooks":328},{"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":32,"project_category":46,"contact_email_category":53,"yst_prominent_words":54,"class_list":62,"better_featured_image":89,"acf":123,"yoast_meta":131,"_links":134},31729,"2020-12-29T10:00:00","2020-12-29T09:00:00",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=31729&_wpnonce=d9af604f89&status=auto-draft&type=post","2021-01-10T22:50:07","2021-01-10T21:50:07","simple-algorithms-vs-a-i","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fsimple-algorithms-vs-a-i",{"rendered":17},"Simple Algorithms vs. A.I. And What They Mean For Medicine",{"rendered":19,"protected":20},"\n\u003Cp>Artificial intelligence (A.I.), machine learning (ML) and algorithms; if you are a regular at The Medical Futurist, you’ve come across these terms more than once whether it’s in our articles, videos or e-books. Indeed, our latest \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002F5-things-we-learnt-about-investments-in-digital-health-new-e-book\" target=\"_blank\">e-book about investment in digital health\u003C\u002Fa> has a dedicated section about A.I. \u003C\u002Fp>\n\n\n\n\u003Cp>Even The Medical Futurist Institute’s \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fa-physicians-visual-guide-to-artificial-intelligence\u002F\" target=\"_blank\">latest peer-reviewed study\u003C\u002Fa> was published as a guide for medical professionals about the technology. But it’s not just us with a fascination for the sci-fi-esque technology, the healthcare A.I. market \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F826993\u002Fhealth-ai-market-value-worldwide\u002F\" target=\"_blank\">is booming\u003C\u002Fa>, as is \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\u002F\" target=\"_blank\">life science research\u003C\u002Fa> around the technology.\u003C\u002Fp>\n\n\n\n\u003Cp>As A.I. becomes quasi-omnipresent in medicine, the hype factor comes into play. With the interest around the technology, companies might throw the term around left and right; claiming their solution uses A.I. when in fact, they only use a spreadsheet with some macros in it. All of these word-plays to entice investors gain more attention and play the marketing game. \u003C\u002Fp>\n\n\n\n\u003Cp>As such, it becomes crucial to take a step back and ask oneself when coming across a claim that a tool is A.I.-based whether the technology in question is really A.I.-based – or just a simple algorithm. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Knowing where to draw the line between an algorithm and an A.I. will help each and every one of us better address the relevant legal, ethical and social implications; especially when it comes to medicine.\u003C\u002Fstrong> \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-embed-youtube aligncenter wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\">\u003Cdiv class=\"wp-block-embed__wrapper\">\n\u003Ciframe loading=\"lazy\" title=\"One-minute Challenge: Artificial Intelligence - The Medical Futurist\" width=\"640\" height=\"360\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FEJuX4xEpajA?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen>\u003C\u002Fiframe>\n\u003C\u002Fdiv>\u003C\u002Ffigure>\n\n\n\n\u003Cp>We aim to clear the confusion around this significant issue with this article. To that end, we also turned to Márton Görög, Data Scientist at the \u003Ca rel=\"noreferrer noopener\" href=\"http:\u002F\u002Fwww.mukkozpont.hu\u002Findex.en.html\" target=\"_blank\">Center for Molecular Fingerprinting\u003C\u002Fa>, to help us take the theoretical stick to draw that theoretical – but much-needed – line.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Knowing the terms\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Even though the terms A.I. and algorithm have picked up steam in recent years, they aren’t new concepts altogether. In fact, ‘algorithm’ \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.merriam-webster.com\u002Fdictionary\u002Falgorithm\" target=\"_blank\">owes its roots\u003C\u002Fa> to the 9th-century Persian mathematician Muḥammad ibn Mūsā al-Khwārizmī; where ‘al-Khwārizmī’ was Latinised as \u003Cem>Algoritmi\u003C\u002Fem>. The current, broad meaning of the word, \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.merriam-webster.com\u002Fdictionary\u002Falgorithm\" target=\"_blank\">as defined by Merriam-Webster\u003C\u002Fa>, refers to “a step-by-step procedure for solving a problem or accomplishing some end.” \u003C\u002Fp>\n\n\n\n\u003Cp>Such instructions are part of what makes an A. I. but aren’t defined as such by themselves. \u003Cstrong>Think of an algorithm as giving a robot a recipe to make a pancake. The robot will follow that recipe, make that pancake and stop once this function has been completed.\u003C\u002Fstrong> But following such a simple algorithm does not mean that the robot possesses artificial intelligence.\u003C\u002Fp>\n\n\n\n\u003Cp>This latter term was coined by computer scientist John McCarthy \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fbernardmarr\u002F2018\u002F02\u002F14\u002Fthe-key-definitions-of-artificial-intelligence-ai-that-explain-its-importance\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">in 1956 during a conference\u003C\u002Fa> with fellow researchers held in Dartmouth, New Hampshire. Merriam-Webster \u003Ca href=\"https:\u002F\u002Fwww.merriam-webster.com\u002Fdictionary\u002Fartificial%20intelligence\" target=\"_blank\" rel=\"noreferrer noopener\">defines A.I.\u003C\u002Fa> as “the capability of a machine to imitate intelligent human behavior”. \u003C\u002Fp>\n\n\n\n\u003Cp>However, when talking about A.I. nowadays, we most often focus on ‘machine learning’, which is one of A.I.’s subcategories, Márton Görög points out. ML is itself \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.merriam-webster.com\u002Fdictionary\u002Fmachine%20learning\" target=\"_blank\">defined as\u003C\u002Fa> “\u003Cem>the process by which a computer is able to improve its own performance (as in analysing image files) by continuously incorporating new data into an existing statistical model\u003C\u002Fem>.” \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>With our pancake recipe example, \u003Cstrong>an ML-based robot fed with enough data about pancake recipes will still make a pancake. But rather than follow the recipe, it will learn it.\u003C\u002Fstrong> The robot will thereafter make that pancake with the right mix of ingredients from specific brands it found to be more favourable from the dataset; even if you haven’t explicitly told it to do so.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Drawing the line between a regular algorithm and an A.I.\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>With those definitions of algorithm and A.I.\u002FML, their differences become clearer. In short, a regular algorithm simply performs a task as instructed, while a true A.I. is coded to learn to perform a task. Márton Görög also points out that ‘learn’ is a very important word for defining an ML-based algorithm. But equally important to him is to add ‘data’, as a regular algorithm doesn’t need data at all to be created. He follows up with defining an ML algorithm as one programmed to  “\u003Cem>learn to perform a task using training data\u003C\u002Fem>.”\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\u002F065_compassionate_care_v2-768x432.png\" alt=\"A.I. in medicine\" class=\"wp-image-23751\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F065_compassionate_care_v2-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F065_compassionate_care_v2-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F065_compassionate_care_v2-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F065_compassionate_care_v2-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002F065_compassionate_care_v2-512x288.png 512w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>“\u003Cem>In this contemporary sense, the main differences are that a regular algorithm is created fully by a software engineer, implementing the known way of solving the problem as machine-readable commands\u003C\u002Fem>,” Görög elaborates. “\u003Cem>While after the preparation of an ML model comes the training itself, which is driven by the training data, often without any human interaction\u003C\u002Fem>.” \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>With an ML model, it’s not the engineer’s input that shapes the algorithm’s decision but rather the data with which it is fed.\u003C\u002Fstrong> It is data that drives and improves the model, without direct human commands.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The importance of this distinction in medicine\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>With the definitions and explanations in place, separating true A.I. from regular algorithms might be a clear-cut affair. But in practice, companies selling their products might hide behind the terminology without properly describing the functioning of their solution. \u003C\u002Fp>\n\n\n\n\u003Cp>This is what Dr. Meskó and his team noticed while creating and updating \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\u002F\" target=\"_blank\">the first database of FDA-approved A.I.-based algorithms\u003C\u002Fa>. Several companies submitting their FDA-approved devices or software only mention their tools as A. I.-based on their website without further explanation of why they credit them as such. Needless to say that these companies omit the mention of their solutions as A. I.-based altogether in their official submission for approval to the FDA.\u003C\u002Fp>\n\n\n\n\u003Cp>The need for transparency around algorithms involved in healthcare becomes paramount for multiple reasons. If one purchases a consumer or clinical product thinking that the device possesses A.I. capabilities but does not output results as expected, who is to blame? Is it the fault of the company for not clearly describing the underlying technology? Or is the customer at fault due to a failure of due diligence from their part?\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"240\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-768x240.jpg\" alt=\"A.I. Bias\" class=\"wp-image-24879\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-768x240.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-1536x480.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F-512x160.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F10-21-2018-Digitalist_Q1_AI-Bias_F.jpg 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption>Source: www.geneticliteracyproject.org\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>It also becomes an ethical and social dilemma when patients become involved. A company selling a product or a medical team using one must be able to explain why ML algorithms keep making different decisions. This is because such algorithms learn from the dataset they are fed with. But patients might be oblivious to the reasoning and might feel as test subjects. Moreover, the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fa-i-bias-in-healthcare\u002F\" target=\"_blank\">intrinsic bias\u003C\u002Fa> in datasets inevitably influences the A.I.’s decisions.\u003C\u002Fp>\n\n\n\n\u003Cp>Márton Görög also noticed concerns in safety-critical industries with those so-called “black box A.I. models”, where safe operation is hard to prove. “\u003Cem>Human-created algorithms are easier to analyse, validate and trust,\u003C\u002Fem>” he told The Medical Futurist. “\u003Cem>With ML-based solutions, the responsibility of the vendor needs to cover the size, quality and diversity of the training data as well.\u003C\u002Fem>”\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Becoming true A.I. seekers\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Ideally, we would rely on the transparency and goodwill of companies developing algorithms and A.I. solutions to be truthful about the underlying methods. But the reality is that we can’t take such claims without a grain of salt. It doesn’t mean that if a software uses only a simple algorithm it’s not useful. On the contrary, it can handle repetitive tasks so that humans don’t have to. But knowing whether a software really uses A.I. or not will allow us to better understand its functioning and what to expect of it.\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\u002F06\u002Flawsuit-robots-768x432.png\" alt=\"Lawsuit robots\" class=\"wp-image-21032\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F06\u002Flawsuit-robots-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F06\u002Flawsuit-robots-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F06\u002Flawsuit-robots-512x288.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F06\u002Flawsuit-robots.png 870w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>When bringing this up with Márton Görög, he agreed that it’s hard to see what’s under the hood, since both of them are essentially software. But he has some tips to help one make the distinction.  “\u003Cem>If it’s assumed that the company owns a data-generating simulator or a rich dataset – regardless if it was collected or bought -, they might really build an ML-model,\u003C\u002Fem>” he explains. “\u003Cem>On the other hand: without data there can’t be training. The task itself can help as well: state-of-the-art results with image\u002Fvideo recognition, speech synthesis, speech recognition and text translation can be achieved only with ML methods\u003C\u002Fem>.”\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Becoming “true A.I. seekers” will become increasingly important in the coming years. This holds true whether you are a medical doctor, digital health enthusiast or patient. The extra legwork might not be totally enticing, but it will ultimately save us from unexpected surprises.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Join the discussion on Linkedin\u003C\u002Fh2>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter size-large is-resized\">\u003Ca class=\"img\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:6752960044687507457\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F01\u002FScreenshot-2021-01-10-at-22.11.44-768x1074.png\" alt=\"\" class=\"wp-image-32013\" width=\"384\" height=\"537\"\u002F\u003C\u002Fa srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F01\u002FScreenshot-2021-01-10-at-22.11.44-768x1074.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F01\u002FScreenshot-2021-01-10-at-22.11.44.png 772w\" sizes=\"auto, (max-width: 384px) 100vw, 384px\" \u002F>\u003C\u002Fa>\u003C\u002Ffigure>\u003C\u002Fdiv>\n",false,{"rendered":22,"protected":20},"\u003Cp>Artificial intelligence (A.I.), machine learning (ML) and algorithms; if you are a regular at The Medical Futurist, you’ve come across these terms more than once […]\u003C\u002Fp>\n",16,31819,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31],504,[33,34,35,36,37,38,39,40,41,42,43,44,45],421,671,821,134,6437,137,6439,196,6441,207,6443,236,6445,[47,48,49,50,51,52],947,948,949,950,951,953,[],[55,56,57,58,59,60,61],1661,1693,1715,1883,2705,2739,1617,[63,14,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88],"post-31729","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-study","tag-machine-learning","tag-a-i","tag-ai","tag-marton-gorog","tag-algorithm","tag-data-science","tag-data-2","tag-center-for-molecular-fingerprinting","tag-digital-health","tag-john-mccarthy","tag-fda-2","tag-black-box","project_category-company","project_category-developers","project_category-educators","project_category-medical-professionals","project_category-patients","project_category-researchers",{"id":24,"alt_text":90,"caption":91,"description":92,"media_type":93,"media_details":94,"post":5,"source_url":122},"A.I. vs. Algorithm","Where's the line between a smart tool and artificial intelligence?","The line between an algorithm and an artificial intelligence is clear. 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vs A.I: What's the Difference & What They Mean for Medicine","Algorithms vs A.I.: There's a fine line between algorithms and an A.I. – This definition will help us better address the issue, especially in medicine.",{"self":135,"collection":141,"about":144,"author":147,"replies":150,"version-history":153,"predecessor-version":157,"wp:featuredmedia":161,"wp:attachment":164,"wp:term":167,"curies":183},[136],{"href":137,"targetHints":138},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F31729",{"allow":139},[140],"GET",[142],{"href":143},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[145],{"href":146},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[148],{"embeddable":26,"href":149},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[151],{"embeddable":26,"href":152},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=31729",[154],{"count":155,"href":156},27,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F31729\u002Frevisions",[158],{"id":159,"href":160},32017,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F31729\u002Frevisions\u002F32017",[162],{"embeddable":26,"href":163},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F31819",[165],{"href":166},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=31729",[168,171,174,177,180],{"taxonomy":169,"embeddable":26,"href":170},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=31729",{"taxonomy":172,"embeddable":26,"href":173},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=31729",{"taxonomy":175,"embeddable":26,"href":176},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=31729",{"taxonomy":178,"embeddable":26,"href":179},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=31729",{"taxonomy":181,"embeddable":26,"href":182},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=31729",[184],{"name":185,"href":186,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[188],{"id":130,"date":189,"date_gmt":190,"guid":191,"modified":193,"modified_gmt":194,"slug":195,"status":13,"type":14,"link":196,"title":197,"content":199,"excerpt":201,"author":203,"featured_media":204,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":205,"categories":206,"tags":208,"project_category":222,"contact_email_category":223,"yst_prominent_words":224,"class_list":231,"better_featured_image":247,"acf":273,"yoast_meta":282,"_links":285},"2019-06-06T15:34:52","2019-06-06T13:34:52",{"rendered":192},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=24083&_wpnonce=50064319a4&status=auto-draft&type=post","2023-02-27T14:31:34","2023-02-27T13:31:34","fda-approvals-for-algorithms-in-medicine","https:\u002F\u002Fmedicalfuturist.com\u002Ffda-approvals-for-algorithms-in-medicine",{"rendered":198},"FDA Approvals For Smart Algorithms In Medicine In One Giant Infographic",{"rendered":200,"protected":20},"\n\u003Ctable style=\"width: 100%; border-collapse: collapse; background-color: #eee; border-top: 4px solid #444;\" cellpadding=\"5px\">\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"width: 100%;\">\n\u003Cp style=\"color: #555;font-size: 12px;line-height: 14px\">THIS ARTICLE HAS NOT BEEN UPDATED SINCE 2019. THE INFORMATION SHARED IN THE ARTICLE WAS ACCURATE AT THE TIME OF ITS PUBLICATION, BUT IT MAY BE OUT OF DATE NOW. \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fmagazine\">BROWSE OUR LATEST ARTICLES HERE\u003C\u002Fa>\u003C\u002Fp>\n\u003C\u002Ftd> \u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\n\n\n\n\u003Cp>Mental health algorithms\nmimicking empathy? A.I. outsmarting human doctors? Simple big data analytical\nsoftware presented with clever marketing tactics? It’s difficult to assess the\nactual state of play when it comes to artificial intelligence in healthcare. Moreover,\nthere’s no database that contains all the smart algorithms worth applying to\nmedical processes. That’s the reason why we decided to collect every artificial\nintelligence-based algorithm that already received FDA approval – meaning that\nthey are proven, reliable, and accurate solutions enabled by an official\nregulator for medical use. Let’s see the infographic in details!\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The\nfactors of algorithmic healing\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>When\nwe started to assess the universe of smart algorithms in healthcare, we took\ninto account temporal and spatial factors, accuracy and credibility, as well as\nmedical specialties where A.I. algorithms have a chance to make the care\nprocess better. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Concerning the timeline, we noticed an uptake in the appearance of new solutions in the last years, and you can see that also in the infographic\u003C\u002Fstrong>. In 2014, only AliveCor’s algorithm for the detection of atrial fibrillation was approved. Two years later, the FDA found further four solutions ready for clinical use, while in 2017, six new algorithms were approved by the US regulator. This exponential growth just accelerated last year, when the FDA endorsed 23 algorithms in medicine. \u003Cstrong>As the first approvals in 2019 also show, we do not expect the trend to slow down. On the contrary, we will most likely see dozens of new medical A.I. solutions on the market.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Looking at the spatial factors, while \u003Ca href=\"https:\u002F\u002Fwww.analyticsinsight.net\u002Ftop-6-artificial-intelligence-hubs-in-2018-an-analysis-for-their-localization\u002F\">the most important hubs for A.I. development\u003C\u002Fa> are the Silicon Valley, the Boston-New York area, Montréal, London, Bangalore, and Beijing, and the same can be applied in medicine and healthcare, \u003Cstrong>the most decisive factor for compiling FDA approved algorithms was that it is the only yardstick for credible and accurate medical software\u003C\u002Fstrong>. Although in Europe, the \u003Ca href=\"https:\u002F\u002Fwww.ema.europa.eu\u002Fen\">European Medicine Agency\u003C\u002Fa> has guidelines and statements about artificial intelligence, \u003Cstrong>the FDA is the only regulator with efficient instruments in its toolkit to access the credibility and accuracy of algorithms for medical purposes in detail\u003C\u002Fstrong>. \u003Cstrong>It also means that we had to stay on the U.S. market and consider the developments within the FDA’s jurisdiction.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"304\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2017\u002F09\u002Fdeep-learning-classifying-lesions-512x304.png\" alt=\"amazing technologies changing dermatology\" class=\"wp-image-16278\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F09\u002Fdeep-learning-classifying-lesions-512x304.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F09\u002Fdeep-learning-classifying-lesions-768x456.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F09\u002Fdeep-learning-classifying-lesions-421x250.png 421w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F09\u002Fdeep-learning-classifying-lesions.png 870w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: Deep Learning Algorithm Classifying Lesions. Fig. 1b, Esteva, Kuprel et al., 2017\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>What does an FDA approval mean?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cstrong>Regarding the meaning of the FDA\napproval itself, the listed algorithms embrace the entire scale of approvals \u003Ca href=\"https:\u002F\u002Fkenblockconsulting.com\u002Ffda-submissions\">starting from\n510(K) submission through de novo to premarket approval (PMA)\u003C\u002Fa>\u003C\u002Fstrong>\u003Cstrong>.\u003C\u002Fstrong> The first one\nrefers to a premarket submission to demonstrate that a device aiming for market\nlaunch but not requiring premarket approval is as safe and effective as other\nsimilar instruments with PMA. The latter actually means the FDA process of\nscientific and regulatory review to evaluate the safety and effectiveness of\nmedical devices supporting and\u002For sustaining human life and the most stringent\nof the device marketing applications. \u003C\u002Fp>\n\n\n\n\u003Cp>The de novo pathway for device marketing rights was added to address novel devices of low to moderate risk that do not have a valid predicate device – for example in the case of software solutions such as smart algorithms. Upon successful review of a de novo submission, FDA creates a classification for the instrument, a regulation if necessary, and identifies any special controls required for future premarket submissions of substantially equivalent devices. As the infographic would have been too complex if we had broken down the FDA approval into various subtypes, we left it as a single category for the time being.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"288\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FAI-In-Medicine-512x288.jpeg\" alt=\"algorithms in medicine\" class=\"wp-image-24085\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FAI-In-Medicine-512x288.jpeg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FAI-In-Medicine-370x208.jpeg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FAI-In-Medicine-768x432.jpeg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FAI-In-Medicine-1536x864.jpeg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FAI-In-Medicine.jpeg 1600w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: www.statnews.com\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Which\nmedical specialties are the most algorithm-friendly?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>When looking at the infographic itself, the distribution of smart algorithms in the various medical specialties becomes visible. \u003Cstrong>Radiology and cardiology seem to be heavily populated by artificial intelligence-based solutions, there are already seven approved algorithms in cardiology, while 16 in radiology. However, geriatrics, orthopedics or pathology seem to be less prone to A.I. Certain medical specialties do not even appear in the list yet, such as pulmonology, dermatology, surgery, OB\u002FGyn or forensic medicine.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Although we should not draw definitive conclusions from only this infographic, as it constitutes a snapshot of the current state of play not revealing anything about the trends. For example, in the case of pathology, the number of FDA-approved algorithms might be low at the moment, artificial intelligence is a promising technology in the field – although it might need the coming years to catch up with the number of solutions in radiology or cardiology.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"288\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2018\u002F05\u002Fai-doctor-image-512x288.jpg\" alt=\"digital health technologies\" class=\"wp-image-17943\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F05\u002Fai-doctor-image-512x288.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F05\u002Fai-doctor-image-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F05\u002Fai-doctor-image-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F05\u002Fai-doctor-image-444x250.jpg 444w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2018\u002F05\u002Fai-doctor-image.jpg 870w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>These two fields represent the peak areas of artificial intelligence research due to several reasons. First and foremost, computer vision is one of the fastest-growing fields in A.I. development, and medical imaging has both the data and the visuality that smart algorithms need to thrive. As a consequence,  \u003Ca href=\"https:\u002F\u002Fspectrum.ieee.org\u002Fthe-human-os\u002Frobotics\u002Fartificial-intelligence\u002Fcomputers-match-human-accuracy-in-screening-for-breast-cancer-risk\">researchers found that commercial software for automatically classifying breast density\u003C\u002Fa>, and thus detecting breast cancer, can perform on par with human radiologists. What’s more, in April 2018, \u003Ca href=\"https:\u002F\u002Fspectrum.ieee.org\u002Fthe-human-os\u002Fbiomedical\u002Fimaging\u002Fmedical-imaging-ai-software-vulnerable-to-covert-attacks\">the FDA approved the first AI system\u003C\u002Fa> that can be used for medical diagnosis without the input of a human clinician. \u003C\u002Fp>\n\n\n\n\u003Cp>Without\nfurther ado, let’s see a rundown of the algorithms.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The list of FDA-approved algorithms in medicine\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Check out our infographic here in detail and see the list below. Click on it for the high-resolution version.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Ca class=\"img\" href=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FThe-Medical-Futurist-FDA-approved-AI-algorithms-in-medicine-2019-09.png\" target=\"_blank\" rel=\"noreferrer noopener\"\u003C\u002Fa>\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"440\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FThe-Medical-Futurist-FDA-approved-AI-algorithms-in-medicine-2019-09-512x440.png\" alt=\"\" class=\"wp-image-25260\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FThe-Medical-Futurist-FDA-approved-AI-algorithms-in-medicine-2019-09-512x440.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FThe-Medical-Futurist-FDA-approved-AI-algorithms-in-medicine-2019-09-768x660.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002FThe-Medical-Futurist-FDA-approved-AI-algorithms-in-medicine-2019-09.png 1257w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>AliveCor supports the \u003Ca href=\"https:\u002F\u002Fwww.alivecor.com\u002Fpress\u002Fpress_release\u002Falivecor-named-no-1-artificial-intelligence-company-in-fast-companys-2018-most-innovative-companies\u002F\">early detection of atrial fibrillation\u003C\u002Fa>, developed an ECG analytics platform – just as PhysiQ Heart Rhythm Module, Apple, and \u003Ca href=\"https:\u002F\u002Fventurebeat.com\u002F2017\u002F07\u002F05\u002Fcardiologs-ecg-analysis-platform-receives-fda-clearance\u002F\">Cardiologs\u003C\u002Fa> – and a six-lead smartphone ECG.\u003C\u002Fli>\n\n\n\n\u003Cli>QbCheck helps with the \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpubmed\u002F23361114\">diagnosis and treatment of ADHD\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>InPen \u003Ca href=\"https:\u002F\u002Fdiatribe.org\u002Fnew-apps-and-devices-diabetesmine-d-data-exchange-and-innovation-summit\">tracks insulin dosage\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>One Drop Blood Glucose \u003Ca href=\"https:\u002F\u002Fonedrop.today\u002Fblogs\u002Fblog\u002Fdecision-support-one-drop-can-predict-the-future-with-automated-decision-support\">quantifies blood glucose levels\u003C\u002Fa> and automatically sends the data to the paired app.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.itnonline.com\u002Fcontent\u002Fphilips-integrates-reacts-tele-ultrasound-platform-lumify-portable-systems\">Lumify\u003C\u002Fa> offers ultrasound image diagnosis.\u003C\u002Fli>\n\n\n\n\u003Cli>Cantab Mobile acts as a tool for \u003Ca href=\"https:\u002F\u002Fwww.cambridgecognition.com\u002Fblog\u002Fentry\u002Fcantab-mobile-experience-of-use-of-a-digital-memory-assessment-tool-in-care\">memory problem assessment for the elderly\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>EnsoSleep powers a \u003Ca href=\"https:\u002F\u002Fxconomy.com\u002Fwisconsin\u002F2018\u002F03\u002F06\u002Fensodata-raises-1-5m-to-develop-a-i-powered-sleep-analysis-tools\u002F\">tool for recognizing sleep disorders\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>AmCAD-US \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpubmed\u002F28071987\">evaluates thyroid nodules\u003C\u002Fa> and categorizes nodule characteristics.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"http:\u002F\u002Fen.lepumedical.com\u002F\">Lepu Medical\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.biotricity.com\u002Fbioflux\u002F\">BioFlux\u003C\u002Fa> detect arrhythmias.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fsubtlemedical.com\u002F\">Subtle Medical\u003C\u002Fa> offers a medical imaging platform.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fbaylabs.io\u002F\">Bay Labs\u003C\u002Fa> offers echocardiogram analysis.\u003C\u002Fli>\n\n\n\n\u003Cli>Viz.AI \u003Ca href=\"https:\u002F\u002Fwww.radiologybusiness.com\u002Fsponsored\u002F22221\u002Ftopics\u002Fartificial-intelligence\u002Fvizai-artificial-intelligence-stroke-software\">detects stroke on CT scans\u003C\u002Fa> and helps clinicians win the race against time.\u003C\u002Fli>\n\n\n\n\u003Cli>Arterys’ algorithm is able to \u003Ca href=\"https:\u002F\u002Fwww.medgadget.com\u002F2018\u002F02\u002Farterys-fda-clearance-liver-ai-lung-ai-lesion-spotting-software.html\">spot cancerous lesions in liver and lungs\u003C\u002Fa> on CT and MR images.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.empatica.com\u002F\">Empatica\u003C\u002Fa> helps detec epileptic seizures.\u003C\u002Fli>\n\n\n\n\u003Cli>Cognoa’s algorithm built into an app \u003Ca href=\"https:\u002F\u002Fwww.globenewswire.com\u002Fnews-release\u002F2019\u002F02\u002F06\u002F1711471\u002F0\u002Fen\u002FCognoa-Receives-FDA-Breakthrough-Designations-for-Autism-Diagnostic-and-Digital-Therapeutic-Devices.html\">helps diagnose autism in kids\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.medtronic-diabetes.co.uk\u002Fabout-diabetes\u002Fcontinuous-glucose-monitoring\">Medtronic\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.presspogo.com\u002F\">POGO\u003C\u002Fa> monitor and predict blood glucose changes.\u003C\u002Fli>\n\n\n\n\u003Cli>Idx autonomously detects diabetic retinopathy using retinal images.\u003C\u002Fli>\n\n\n\n\u003Cli>Icometrix helps \u003Ca href=\"https:\u002F\u002Ficometrix.com\u002Fnews\u002Ficometrix-expands-AI-portfolio-with-new-icobrain-reports-to-aid-the-diagnosis-of-dementia\">neurologists interpret brain MR images\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>Imagen \u003Ca href=\"https:\u002F\u002Fhealthitanalytics.com\u002Fnews\u002Ffda-clears-marketing-for-ai-algorithm-to-detect-wrist-fractures\">aids healthcare providers in identifying wrist fractures\u003C\u002Fa> with similar accuracy as human radiologists.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fneuronewsinternational.com\u002Fneuralbot-system-receives-fda-clearance\u002F\">NeuralBot\u003C\u002Fa> offers a solution for transcranial Doppler probe positioning.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.mindmotionweb.com\u002Fmindmotion-go\u002F\">MindMotion Go\u003C\u002Fa> advances its algorithm for motion capture for the elderly.\u003C\u002Fli>\n\n\n\n\u003Cli>Dreamed assists \u003Ca href=\"http:\u002F\u002Fwww.israelscienceinfo.com\u002Fen\u002Fmedecine\u002Fdreamed-diabetes-israel-recoit-le-marquage-ce-pour-sa-plateforme-de-gestion-du-diabete-de-type-1\u002F\">healthcare professionals in the management of Type 1 diabetes\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>Zebra Medical Vision detects, quantifies coronary artery calcification, and analyses chest X-rays.\u003C\u002Fli>\n\n\n\n\u003Cli>Aidoc is able to \u003Ca href=\"https:\u002F\u002Fwww.prnewswire.com\u002Fnews-releases\u002Fa-first-in-the-world-of-radiology-aidoc-receives-fda-clearance-to-enable-radiologists-to-triage-patients-using-ai-300693930.html\">flag brain bleeding in head CT images\u003C\u002Fa> and pulmonary embolism.\u003C\u002Fli>\n\n\n\n\u003Cli>iCAD classifies breast density and detects breast cancer as accurately as radiologists.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.itnonline.com\u002Fcontent\u002Fscreenpoint-medical-receives-fda-clearance-transpara-mammography-ai-solution\">ScreenPoint Medical\u003C\u002Fa> assists radiologists with the reading of screening mammograms.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.accessdata.fda.gov\u002Fcdrh_docs\u002Fpdf18\u002FK180647.pdf\">Briefcase\u003C\u002Fa> triages and diagnoses time-sensitive patients.\u003C\u002Fli>\n\n\n\n\u003Cli>RightEye Vision System \u003Ca href=\"https:\u002F\u002Fwww.medtechdive.com\u002Fnews\u002Frighteyes-eye-tracking-system-gets-fda-clearance\u002F539306\u002F\">tracks eye movements for identifying visual tracking impairment\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fmaxq.ai\u002F\">MaxQ\u003C\u002Fa> develops an acute intracranial hemorrhage triage algorithm.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.icadmed.com\u002Fprofoundai.html\">ProFound AI\u003C\u002Fa> detects and diagnoses suspicious lesions.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.resetforrecovery.com\u002F\">ReSET-O\u003C\u002Fa> offers an adjuvant treatment of substance abuse disorder.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fverily.com\u002Fprojects\u002Fsensors\u002Fstudy-watch\u002F\">Verily\u003C\u002Fa> developed an ECG feature on the \u003Ca href=\"https:\u002F\u002Fverily.com\u002Fprojects\u002Fsensors\u002Fstudy-watch\u002F\">Study Watch\u003C\u002Fa>.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fpaige.ai\u002F\">Paige.AI\u003C\u002Fa> provides a clinical-grade algorithm in pathology.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Ca href=\"http:\u002F\u002Fclients3.weblink.com.au\u002Fpdf\u002FRHT\u002F02055462.pdf\">FerriSmart\u003C\u002Fa> created a machine learning solution for the quantification of liver iron concentration.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>If you find any inaccuracies or you think we missed one, please do let us know so we can update the infographic. Thank you!\u003C\u002Fp>\n\n\n\n\u003Ctable style=\"width: 100%; border-collapse: collapse; background-color: #eee; border-top: 4px solid #444;\" cellpadding=\"10px\">\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd style=\"width: 100%;\">\n\u003Cp>\u003Cstrong>At The Medical Futurist, we are building a community for making a bold vision about the future of healthcare reality today.\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Ciframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FFIbMejImnxs\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen=\"\">\u003C\u002Fiframe>\n\u003Cp>If you’d like to support this mission, we invite you to \u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fthemedicalfuturist\">join The Medical Futurist Patreon Community\u003C\u002Fa>. A community of empowered patients, future-oriented healthcare professionals, concerned health policymakers, sensible health tech developers, and enthusiastic medical students. If there were ever a time to join us, it is now. Every contribution, however big or small, powers our research and sustains our future.\u003C\u002Fp>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fthemedicalfuturist\">\u003Cstrong style=\"background-color: #e71d3299; color: #000;\">Click here to support The Medical Futurist from as little as $3\u003C\u002Fstrong>\u003C\u002Fa> – it only takes a minute. Thank you.\u003C\u002Fp>\n\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003Cp> \u003C\u002Fp>\n",{"rendered":202,"protected":20},"\u003Cp>Mental health algorithms mimicking empathy? A.I. outsmarting human doctors? Simple big data analytical software presented with clever marketing tactics? We decided to collect every artificial intelligence-based algorithm that already received FDA approval – meaning that they are proven, reliable, and accurate solutions enabled by an official regulator for medical use.\u003C\u002Fp>\n",6,24087,{"_acf_changed":20,"footnotes":27},[31,207],521,[209,210,211,212,213,36,214,215,216,217,218,44,219,220,221],271,1353,275,372,411,1168,144,1203,166,1228,1257,246,1350,[],[],[57,225,226,58,227,228,229,230],1723,1819,2261,2689,4699,5067,[232,14,64,65,66,67,68,69,233,234,235,236,237,238,73,239,240,241,242,243,81,244,245,246],"post-24083","category-future-medicine","tag-health","tag-approval","tag-healthcare","tag-radiology","tag-software","tag-smart-algorithm","tag-artificial-intelligence","tag-smart","tag-cardiology","tag-artificial","tag-digital-solutions","tag-future","tag-smart-health",{"id":204,"alt_text":248,"caption":27,"description":249,"media_type":93,"media_details":250,"post":130,"source_url":272},"algorithms in medicine","The first open access, online database of FDA-approved A.I.-based algorithms The Medical Futurist Institute (TMFI) pioneered the first open-access, online database of FDA-approved A.I.-based algorithms.",{"width":95,"height":96,"file":251,"sizes":252,"image_meta":271},"2019\u002F06\u002F0606_FDA_infographic_16x9.png",{"medium":253,"large":256,"thumbnail":259,"medium_large":262,"1536x1536":263,"2048x2048":266},{"file":254,"width":101,"height":102,"mime-type":103,"source_url":255},"0606_FDA_infographic_16x9-370x208.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F0606_FDA_infographic_16x9-370x208.png",{"file":257,"width":107,"height":108,"mime-type":103,"source_url":258},"0606_FDA_infographic_16x9-768x432.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F0606_FDA_infographic_16x9-768x432.png",{"file":260,"width":112,"height":112,"mime-type":103,"source_url":261},"0606_FDA_infographic_16x9-150x150.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F0606_FDA_infographic_16x9-150x150.png",{"file":257,"width":107,"height":108,"mime-type":103,"source_url":258},{"file":264,"width":117,"height":118,"mime-type":103,"source_url":265},"0606_FDA_infographic_16x9-1536x864.png","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F0606_FDA_infographic_16x9-1536x864.png",{"file":267,"width":268,"height":269,"mime-type":103,"source_url":270},"0606_FDA_infographic_16x9-2048x1152.png","2048","1152","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F0606_FDA_infographic_16x9-2048x1152.png",{"aperture":121,"credit":27,"camera":27,"caption":27,"created_timestamp":121,"copyright":27,"focal_length":121,"iso":121,"shutter_speed":121,"title":27,"orientation":121},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F06\u002F0606_FDA_infographic_16x9-scaled.png",{"related_posts":274,"related_posts_footer":278,"cta_type":27,"cta_color":27,"subtitle":27,"related_books":20},[275,276,277],22987,13172,17629,[279,280,281],13650,14848,10785,{"yoast_wpseo_title":283,"yoast_wpseo_metadesc":284,"yoast_wpseo_canonical":196},"FDA Approvals For Smart Algorithms In Medicine In One Giant Infographic - The Medical Futurist","The Medical Futurist decided to collect all FDA-approved smart algorithms in medicine in one single infographic. Check it out!",{"self":286,"collection":291,"about":293,"author":295,"replies":298,"version-history":301,"predecessor-version":305,"wp:featuredmedia":309,"wp:attachment":312,"wp:term":315,"curies":326},[287],{"href":288,"targetHints":289},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F24083",{"allow":290},[140],[292],{"href":143},[294],{"href":146},[296],{"embeddable":26,"href":297},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F6",[299],{"embeddable":26,"href":300},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=24083",[302],{"count":303,"href":304},17,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F24083\u002Frevisions",[306],{"id":307,"href":308},49635,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F24083\u002Frevisions\u002F49635",[310],{"embeddable":26,"href":311},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F24087",[313],{"href":314},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=24083",[316,318,320,322,324],{"taxonomy":169,"embeddable":26,"href":317},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=24083",{"taxonomy":172,"embeddable":26,"href":319},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=24083",{"taxonomy":175,"embeddable":26,"href":321},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=24083",{"taxonomy":178,"embeddable":26,"href":323},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=24083",{"taxonomy":181,"embeddable":26,"href":325},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=24083",[327],{"name":185,"href":186,"templated":26},[329],{"id":127,"date":330,"date_gmt":331,"guid":332,"modified":334,"modified_gmt":335,"slug":336,"status":13,"type":337,"link":338,"title":339,"content":341,"excerpt":343,"author":345,"featured_media":346,"comment_status":25,"ping_status":25,"template":27,"yst_prominent_words":347,"class_list":349,"better_featured_image":352,"acf":392,"yoast_meta":413,"_links":416},"2021-03-03T21:58:00","2021-03-03T20:58:00",{"rendered":333},"https:\u002F\u002Fapi.medicalfuturist.com\u002F?post_type=book&p=24762","2024-05-29T23:37:45","2024-05-29T21:37:45","a-guide-to-artificial-intelligence-in-healthcare","book","https:\u002F\u002Fapi.medicalfuturist.com\u002Fbooks\u002Fa-guide-to-artificial-intelligence-in-healthcare\u002F",{"rendered":340},"A Guide to Artificial Intelligence in Healthcare",{"rendered":342,"protected":20},"\n\u003Cp>Can we stay human in the\nage of A.I.? To go even further, can we grow in humanity, can we shape a\nmore humane, more equitable and sustainable healthcare?\u003C\u002Fp>\n\n\n\n\u003Cp>Our e-book aims to prepare\nhealthcare and medical professionals for the era of human-machine collaboration.\nRead The Medical Futurist’s guide to understanding, anticipating and\ncontrolling artificial intelligence.\u003C\u002Fp>\n",{"rendered":344,"protected":20},"\u003Cp>Can we stay human in the age of A.I.? To go even further, can we grow in humanity, can we shape a more humane, more equitable 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it on Leanpub",{"yoast_wpseo_title":414,"yoast_wpseo_metadesc":415,"yoast_wpseo_canonical":338},"A Guide to Artificial Intelligence in Healthcare - The Medical Futurist","The Guide To Artificial Intelligence In Healthcare aims to prepare healthcare and medical professionals for the era of human-machine 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