[{"data":1,"prerenderedAt":403},["ShallowReactive",2],{"slug-the-8-most-reassuring-examples-of-using-a-i-in-healthcare":3},{"post":4,"relatedPosts":152,"relatedBooks":307},{"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":33,"contact_email_category":36,"yst_prominent_words":37,"class_list":46,"better_featured_image":56,"acf":87,"yoast_meta":97,"_links":99},47519,"2022-10-14T10:00:00","2022-10-14T08:00:00",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=47519&_wpnonce=78901681de&status=auto-draft&type=post","2022-10-14T10:51:25","2022-10-14T08:51:25","the-8-most-reassuring-examples-of-using-a-i-in-healthcare","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fthe-8-most-reassuring-examples-of-using-a-i-in-healthcare",{"rendered":17},"The 8 Most Reassuring Examples of Using A.I. In Healthcare",{"rendered":19,"protected":20},"\n\u003Cp>Artificial intelligence, probably the most powerful technology trend today, will transform healthcare. In some areas, it has already arrived, extending the diagnostic capabilities of \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-radiology-and-ai\" target=\"_blank\" rel=\"noreferrer noopener\">radiologists \u003C\u002Fa>or \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fhow-an-a-i-based-skin-checking-app-can-work-with-a-national-healthcare-system\" target=\"_blank\" rel=\"noreferrer noopener\">dermatologists\u003C\u002Fa>, supporting\u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffuture-emergency-medicine-innovations-making-patients-point-care\u002F\" target=\"_blank\" rel=\"noreferrer noopener\"> triage decisions\u003C\u002Fa> in emergency units,  looking for \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Frobotics-blockchain-redesign-pharma-supply-chain\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">promising new drug candidates\u003C\u002Fa> or \u003Ca href=\"https:\u002F\u002Fwww.economist.com\u002Ftechnology-quarterly\u002F2022\u002F09\u002F21\u002Fwhat-does-a-brain-computer-interface-feel-like\" target=\"_blank\" rel=\"noreferrer noopener\">allowing locked-in patients to communicate\u003C\u002Fa> with others. \u003C\u002Fp>\n\n\n\n\u003Cp>But this is just the beginning. A cultural and technological revolution is just around the corner. What will it bring? How will it reshape the art of medicine? This is the topic of our most popular e-book, \u003Ca href=\"https:\u002F\u002Fleanpub.com\u002FArtificialIntelligenceinHealthcare\" target=\"_blank\" rel=\"noreferrer noopener\">A Guide To Artificial Intelligence In Healthcare\u003C\u002Fa>. The latest update, as always, aims to bring you up-to-speed and offers a full-scope overview of what to expect and how to adapt to it. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Ca class=\"img\" href=\"https:\u002F\u002Fleanpub.com\u002FArtificialIntelligenceinHealthcare\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F10\u002F1003_tmf_ai_ebook_001-768x432.png\" alt=\"\" class=\"wp-image-24981\"\u002F\u003C\u002Fa srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F10\u002F1003_tmf_ai_ebook_001-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F10\u002F1003_tmf_ai_ebook_001-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F10\u002F1003_tmf_ai_ebook_001-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F10\u002F1003_tmf_ai_ebook_001-512x288.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F10\u002F1003_tmf_ai_ebook_001.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n\n\n\n\u003Cp>For this article I have picked 8 exciting examples of algorithms lending a hand to healthcare professionals, depicting applications already in clinical use and providing value to medical professionals and patients. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">1. A.I. can support the early detection of atrial fibrillation\u003C\u002Fh3>\n\n\n\n\u003Cp>Atrial fibrillation (AFib) is one of the conditions that can increase the risk of stroke, heart failure, and other heart-related complications. Until recently, it was overwhelmingly difficult to deal with AFib, as it needs continuous electrocardiogram (ECG) monitoring to provide data about heart rate and rhythm. \u003C\u002Fp>\n\n\n\n\u003Cp>That has changed with the appearance of digital health devices. For example, AliveCor’s Kardia is an FDA-approved, medical-grade ECG recorder – and the latest model is literally the size of a credit card, \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4kUSA5zZYIQ\" target=\"_blank\" rel=\"noreferrer noopener\">see our review here\u003C\u002Fa>. It can tell you within a minute whether your ECG is normal, whether you possibly have AFib or experience some “unclassified” risks.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"513\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FCardiaMobile-Card-768x513.jpg\" alt=\"\" class=\"wp-image-46117\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FCardiaMobile-Card-768x513.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FCardiaMobile-Card-1536x1025.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FCardiaMobile-Card-2048x1367.jpg 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F03\u002FCardiaMobile-Card.jpg 1618w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>The Kardia algorithm runs pretty much invisibly in the background, analysing readings on the go. However, it is “the most sophisticated A.I. ever brought to personal ECG” – according to the company. “This suite of algorithms and visualizations will provide the platform for delivery of new consumer and professional service offerings beyond AFib, by allowing a much wider range of cardiac conditions to be determined on a personal ECG device.”\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">2. A.I. reduced hospital deaths in sepsis \u003C\u002Fh3>\n\n\n\n\u003Cp>Artificial Intelligence (A.I.) has the potential to highly optimise processes in hospitals and even eliminate problematic alarm fatigue. By enhancing efficiency, A.I. will immensely benefit nurses.\u003C\u002Fp>\n\n\n\n\u003Cp>Duke University researchers demonstrated such an application of A.I. in nursing. Their Sepsis Watch deep learning algorithm helps assess a patient’s risk for developing sepsis. It automatically alerts the hospital’s rapid response team in case of a high-risk patient and guides them through the first 3 hours of care administration. This is critical in preventing complications.\u003C\u002Fp>\n\n\n\n\u003Cp>The university has been working on this algorithm for years and implemented the model in clinical work in 2018. \u003Ca href=\"https:\u002F\u002Fwww.advisory.com\u002Fdaily-briefing\u002F2022\u002F04\u002F12\u002Fai-hospitals\" target=\"_blank\" rel=\"noreferrer noopener\">According to Mark Sendak\u003C\u002Fa>, a physician and clinical data scientist at Duke who co-led the project, Duke is conducting a final analysis, but he noted that mortality seems to be down.\u003C\u002Fp>\n\n\n\n\u003Cp>Hospital chain HCA Healthcare also developed a predictive algorithm called Sepsis Prediction and Optimisation of Therapy. It continuously monitors patient data to identify potentially impending sepsis cases. The algorithm is able to detect sepsis six hours earlier—and more accurately—than clinicians, enabling the health care system to cut sepsis mortality rates across 160 hospitals by nearly 30% – \u003Ca href=\"https:\u002F\u002Fwww.wsj.com\u002Farticles\u002Fhow-hospitals-are-using-ai-to-save-lives-11649610000\" target=\"_blank\" rel=\"noreferrer noopener\">The Wall Street Journal reported\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">3. Pediatric seizure-detecting smart bands \u003C\u002Fh3>\n\n\n\n\u003Cp>Epilepsy is the 4th most common neurological problem, following migraine, stroke and Alzheimer’s disease in the frequency of occurrence in the United States.\u003C\u002Fp>\n\n\n\n\u003Cp>Wearable devices, like the Empatica’s Embrace wristbands, are designed to notify the user and\u002For relatives and caretakers about a seizure or the risk of seizure. \u003C\u002Fp>\n\n\n\n\u003Cp>Clinical testing of Embrace on 141 patients diagnosed with epilepsy, including 80 pediatric patients, yielded a 98% accuracy rate for detecting generalised tonic-clonic seizures, \u003Ca href=\"https:\u002F\u002Fwww.medicaldesignandoutsourcing.com\u002Fempaticas-pediatric-seizure-predicting-smartband-wins-fda-nod\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">the company said\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">4. Skin-checking algorithms help out dermatologists\u003C\u002Fh3>\n\n\n\n\u003Cp>Skin-checking applications allow users to take pictures of their suspicious skin lesions, upload these pictures to a server, the images are first evaluated by an A.I. algorithm and the results will be later validated by a dermatologist. \u003C\u002Fp>\n\n\n\n\u003Cp>These algorithms work by comparing user images to the vast database in the background, coming up with a preliminary diagnosis in just a few seconds. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F06\u002Ftmf_article_274-01-768x432.png\" alt=\"Emerging Trend Alert – Skin Checking Algorithms\" class=\"wp-image-34783\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F06\u002Ftmf_article_274-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F06\u002Ftmf_article_274-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F06\u002Ftmf_article_274-01-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F06\u002Ftmf_article_274-01.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption>Skin Checking Algorithms\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Cp>We could even conclude that if such a system provides follow-up and access to doctors and treatments should the need arise, these are close to the optimal setup – they filter out non-existing cases and let dermatologists focus on the real issues. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">5. A.I. detects stroke on CT scans and helps clinicians win the race against time\u003C\u002Fh3>\n\n\n\n\u003Cp>Viz.ai’s flagship solution, \u003Ca href=\"https:\u002F\u002Fwww.viz.ai\u002Flvo-ctp#LVO\" target=\"_blank\" rel=\"noreferrer noopener\">Viz LVO\u003C\u002Fa>, uses A.I. to automatically detect suspected large vessel occlusion (LVO) strokes on computed tomography angiography (CTA) imaging and directly alert on-call stroke specialists about potentially treatable patients in a standalone or multi-hospital network.\u003C\u002Fp>\n\n\n\n\u003Cp>The first major news about the algorithm came out when the Southeast Regional Stroke Center at Erlanger\u003Ca href=\"https:\u002F\u002Fradiologybusiness.com\u002Fsponsored\u002F22221\u002Fvizai\u002Ftopics\u002Fartificial-intelligence\u002Fvizai-artificial-intelligence-stroke-software\" target=\"_blank\" rel=\"noreferrer noopener\"> started using the algorithm in 2018\u003C\u002Fa>. Many healthcare institutions have followed suit since then. A recent, large, real-world multi-centre study using Viz.ai found a median time-to-notification of five minutes and 45 seconds across all of the sites involved when using Viz LVO – \u003Ca href=\"https:\u002F\u002Fwww.viz.ai\u002Fpress-release\u002Fviz-ai-receives-ce-mark-to-bring-life-saving-stroke-care-to-europe\" target=\"_blank\" rel=\"noreferrer noopener\">the company published\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>In the study, containing the largest health A.I. data set to date, Viz LVO achieved 96 percent sensitivity and 94 percent specificity in identifying LVOs in 2,544 consecutive patients from 139 hospitals using scanners from multiple manufacturers. Faster triage with Viz LVO enables the identification and treatment of more patients who are eligible for thrombectomy, which improves patient outcomes and reduces the chances of long-term disability. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">6. A.I. autonomously detects diabetic retinopathy using retinal images\u003C\u002Fh3>\n\n\n\n\u003Cp>Artificial intelligence (AI) screening algorithms are a promising solution to the growing global diabetic retinopathy (DR) screening burden. Many AI algorithms have been shown to perform at or above the level of human experts on DR classification tasks when evaluated on their internal datasets.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fretinatoday.com\u002Farticles\u002F2022-sept\u002Fthe-path-to-validating-ai-screening-systems\" target=\"_blank\" rel=\"noreferrer noopener\">This study aimed to analyse how accurately these algorithms work\u003C\u002Fa> by validating seven commercially available DR screening algorithms on a large-scale dataset collected from two Veterans Affairs (VA) hospitals. Researchers examined seven commercially available algorithms – two of which have FDA clearance – and found significant differences between their performance. \u003C\u002Fp>\n\n\n\n\u003Cp>Their findings stress that although automated diabetic retinopathy (DR) screening systems can greatly expand access, they do not replace routine eye examinations. Current commercial DR screening systems are approved only to diagnose referrable DR using specific devices and protocols. Sole reliance on automated screening systems may miss additional important features, such as undiagnosed glaucoma, macular degeneration, retinal detachments, or choroidal melanomas. DR screening systems should supplement traditional eye examinations to expand screening access, while also upholding a high standard of care.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">7. A.I. helps pathologists identify metastatic breast cancer\u003C\u002Fh3>\n\n\n\n\u003Cp>Breast cancer is the most prevalent cancer diagnosis for women. According to the latest Global Cancer (GLOBOCAN) statistics from the World Health Organization (WHO), accounting for 11.7% of the incidence and 15.5% of the mortality, ranking first among all cancers.\u003C\u002Fp>\n\n\n\n\u003Cp>Deep learning models aiming to find early signs of the disease are around for a \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.05718\" target=\"_blank\" rel=\"noreferrer noopener\">good number of years\u003C\u002Fa>. Although we have not yet arrived at an omnipotent solution as of yet, studies show that combining deep learning systems’ predictions with human pathologists’ diagnoses improves patient outcomes.\u003C\u002Fp>\n\n\n\n\u003Cp>There is still a long way to go, \u003Ca href=\"https:\u002F\u002Fgs.amegroups.com\u002Farticle\u002Fview\u002F91882\u002Fhtml\" target=\"_blank\" rel=\"noreferrer noopener\">as this study points out\u003C\u002Fa> “DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further research.”\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">8. A.I. builds complex and consolidated platforms for drug discovery\u003C\u002Fh3>\n\n\n\n\u003Cp>Time and efficiency are key in the operation of the pharmaceutical supply chain. Its main objective is to deliver the right medication to the person in need as fast as possible – to aid the healing process in the best way possible. While the drug designing, manufacturing, and distribution supply chains have been changing constantly due to new technologies, the scope and quality of the \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Frobotics-blockchain-redesign-pharma-supply-chain\u002F#\" target=\"_blank\" rel=\"noreferrer noopener\">recent transformation are much more profound\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"536\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAI-in-drug-discovery-cover-768x536.png\" alt=\"top companies drug discovery\" class=\"wp-image-24865\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAI-in-drug-discovery-cover-768x536.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAI-in-drug-discovery-cover-512x357.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FAI-in-drug-discovery-cover.png 1320w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>A.I. solutions could fundamentally alter the traditional process of designing drugs. It could make drug development much cheaper and more effective; remarkably shorten the drug production circle, and help out pharma in finding new drugs. All this without burdening clinical trials and accumulating costs.\u003C\u002Fp>\n\n\n\n\u003Cp>According to \u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2452302X1600036X#:~:text=Although%20the%20drug%20development%20takes,daunting%20and%20difficult%20to%20navigate.\" target=\"_blank\" rel=\"noreferrer noopener\">estimates\u003C\u002Fa>, it takes about 12 years and $2.9 billion for an experimental drug to advance from concept to market. In 2019, A.I. pharma startup Insilico Medicine identified a potential new drug in only 46 days. This is the difference A.I. is capable of. \u003C\u002Fp>\n\n\n\n\u003Cp>San Francisco-based Atomwise uses supercomputers that root out therapies from a database of molecular structures. During the Ebola epidemic in 2015, \u003Ca href=\"https:\u002F\u002Fwww.atomwise.com\u002F2015\u002F03\u002F24\u002Fnew-ebola-treatment-using-artificial-intelligence\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Atomwise used its A.I. algorithm\u003C\u002Fa> to identify two drugs with significant potential to reduce Ebola infectivity. This analysis which typically would have taken months or years was completed in less than one day.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">There is so much to look forward to\u003C\u002Fh3>\n\n\n\n\u003Cp>These are just a few exciting examples of how artificial intelligence will enhance the capabilities of healthcare professionals. The field is vast, and it doesn’t take a futurologist to predict how profound this transformation will be. There is still a long way to cover, not only from the technological point of view but also in working out how to properly regulate algorithms, how to integrate new solutions to everyday clinical practice and how to relate to this paradigm shift as medical professionals and\u002For patients. We hope this brand new update of our ‘\u003Ca href=\"https:\u002F\u002Fleanpub.com\u002FArtificialIntelligenceinHealthcare\" target=\"_blank\" rel=\"noreferrer noopener\">A Guide to Artificial Intelligence in Healthcare\u003C\u002Fa>’ will help you on this journey.\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>A cultural and technological revolution is just around the corner. What will it bring? How will A.I. reshape the art of medicine?\u003C\u002Fp>\n",6,47637,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31],7079,[],[34,35],950,951,[],[38,39,40,41,42,43,44,45],1789,1809,1813,2689,2705,1661,1709,1715,[47,14,48,49,50,51,52,53,54,55],"post-47519","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tmf","project_category-medical-professionals","project_category-patients",{"id":24,"alt_text":27,"caption":27,"description":27,"media_type":57,"media_details":58,"post":5,"source_url":86},"image",{"width":59,"height":60,"file":61,"sizes":62,"image_meta":84},1920,1080,"2022\u002F10\u002F1003_tmf_ai_ebook_003.png",{"medium":63,"large":69,"thumbnail":74,"medium_large":78,"1536x1536":79},{"file":64,"width":65,"height":66,"mime-type":67,"source_url":68},"1003_tmf_ai_ebook_003-370x208.png","370","208","image\u002Fpng","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F10\u002F1003_tmf_ai_ebook_003-370x208.png",{"file":70,"width":71,"height":72,"mime-type":67,"source_url":73},"1003_tmf_ai_ebook_003-768x432.png","768","432","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F10\u002F1003_tmf_ai_ebook_003-768x432.png",{"file":75,"width":76,"height":76,"mime-type":67,"source_url":77},"1003_tmf_ai_ebook_003-150x150.png","150","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F10\u002F1003_tmf_ai_ebook_003-150x150.png",{"file":70,"width":71,"height":72,"mime-type":67,"source_url":73},{"file":80,"width":81,"height":82,"mime-type":67,"source_url":83},"1003_tmf_ai_ebook_003-1536x864.png","1536","864","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F10\u002F1003_tmf_ai_ebook_003-1536x864.png",{"aperture":85,"credit":27,"camera":27,"caption":27,"created_timestamp":85,"copyright":27,"focal_length":85,"iso":85,"shutter_speed":85,"title":27,"orientation":85},"0","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F10\u002F1003_tmf_ai_ebook_003.png",{"cta_type":88,"cta_color":27,"related_books":89,"related_posts_footer":93,"related_posts":20,"subtitle":27},"subscribe",[90,91,92],24762,47427,30419,[94,95,96],46538,47157,15909,{"yoast_wpseo_title":17,"yoast_wpseo_metadesc":98,"yoast_wpseo_canonical":15},"A cultural and technological revolution is just around the corner. What will it bring? How will A.I. reshape the art of medicine?",{"self":100,"collection":106,"about":109,"author":112,"replies":115,"version-history":118,"predecessor-version":122,"wp:featuredmedia":126,"wp:attachment":129,"wp:term":132,"curies":148},[101],{"href":102,"targetHints":103},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F47519",{"allow":104},[105],"GET",[107],{"href":108},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[110],{"href":111},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[113],{"embeddable":26,"href":114},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F6",[116],{"embeddable":26,"href":117},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=47519",[119],{"count":120,"href":121},14,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F47519\u002Frevisions",[123],{"id":124,"href":125},47643,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F47519\u002Frevisions\u002F47643",[127],{"embeddable":26,"href":128},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F47637",[130],{"href":131},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=47519",[133,136,139,142,145],{"taxonomy":134,"embeddable":26,"href":135},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=47519",{"taxonomy":137,"embeddable":26,"href":138},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=47519",{"taxonomy":140,"embeddable":26,"href":141},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=47519",{"taxonomy":143,"embeddable":26,"href":144},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=47519",{"taxonomy":146,"embeddable":26,"href":147},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=47519",[149],{"name":150,"href":151,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[153],{"id":96,"date":154,"date_gmt":155,"guid":156,"modified":158,"modified_gmt":159,"slug":160,"status":13,"type":14,"link":161,"title":162,"content":164,"excerpt":166,"author":23,"featured_media":168,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":169,"categories":170,"tags":172,"project_category":188,"contact_email_category":191,"yst_prominent_words":192,"class_list":199,"better_featured_image":219,"acf":254,"yoast_meta":262,"_links":265},"2025-10-06T09:29:02","2025-10-06T07:29:02",{"rendered":157},"http:\u002F\u002Fmedicalfuturist.com\u002F?p=15909","2025-10-06T09:29:03","2025-10-06T07:29:03","the-future-of-radiology-and-ai","https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-radiology-and-ai",{"rendered":163},"The Future of Radiology And Artificial Intelligence",{"rendered":165,"protected":20},"\n\n\n\u003Cp>What if an algorithm could tell you whether you have cancer based on your CT scan or mammography exam? While I am confident that radiologists’ creative work will be necessary in the future to solve complex issues and supervise diagnostic processes, A.I. will definitely become part of their daily routine in diagnosing simpler cases and taking over repetitive tasks. So rather than getting threatened by it, we should familiarise ourselves with how it could help change the course of radiology for the better.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Radiologists who use AI will replace those who don’t\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>There is a lot of hype and plenty of fear around artificial intelligence and its impact on the future of healthcare. There are many signs pointing toward the fact that \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-will-redesign-healthcare\u002F\" target=\"_blank\">A.I. will completely move the world of medicine\u003C\u002Fa>. As deep learning algorithms and narrow A.I. started to buzz especially around the field of medical imaging, many radiologists went into panic mode. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"768\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002FAI-not-replace-physicians-768x768.jpg\" alt=\"\" class=\"wp-image-47245\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002FAI-not-replace-physicians-768x768.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002FAI-not-replace-physicians-150x150.jpg 150w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002FAI-not-replace-physicians.jpg 800w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Bradley Erickson, Director of the Radiology Informatics Lab at Mayo Clinic told me that some of the hype we hear from some of the machine learning and deep learning experts saying that A.I. would replace radiologists is looking at radiologists as if they were just looking at pictures. \u003Cem>That would be me saying while I look at programmers, all they do is typing, so we can replace a programmer with a speech recognition system\u003C\u002Fem>, he added. Langlotz compared the situation to that of the autopilot in aviation. The innovation did not replace real pilots, it only augmented their tasks. On very long flights, it is handy to turn on the autopilot, but they are useless when you need rapid judgment. So, the combination of humans and machines is the winning solution. And it will be the same in healthcare.\u003C\u002Fp>\n\n\n\n\u003Cp>Thus, I agree with Langlotz completely when he says that \u003Cem>artificial intelligence will not replace radiologists. Yet, those radiologists who use A.I. will replace the ones who don’t\u003C\u002Fem>. Let me show you why.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>What do cat intestines, X-ray lamps and the history of medical imaging have in common?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>The field of clinical radiology started obviously with the quite \u003Ca href=\"http:\u002F\u002Fstatic.springer.com\u002Fsgw\u002Fdocuments\u002F1426506\u002Fapplication\u002Fpdf\u002FVan+Gelderen_A+Brief+History+of+Radiology.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">coincidental discovery of the X-ray by Wilhelm Conrad Röntgen on 8 November 1895 in Würzburg, Germany\u003C\u002Fa>. Within two months, the X-ray mania ran over the world. Sensational headlines in newspapers propagated the “new light seeing through flesh to bones”, while one inventor even speculated that “soon every house will have a cathode-ray machine”. Any similarities about hyped technologies coming to mind?\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"870\" height=\"536\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fwilhelm-conrad-röntgen-and-x-ray.png\" alt=\"Future of Radiology\" class=\"wp-image-15911\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fwilhelm-conrad-röntgen-and-x-ray.png 870w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fwilhelm-conrad-röntgen-and-x-ray-768x473.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fwilhelm-conrad-röntgen-and-x-ray-512x315.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fwilhelm-conrad-röntgen-and-x-ray-406x250.png 406w\" sizes=\"auto, (max-width: 870px) 100vw, 870px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Thomas Edison became so excited about the new discovery that he even wanted to create a commercial “X-ray lamp” (unfortunately, his efforts failed) and tried to get an X-ray of the human brain in action (sadly, that was not a success either). His latter endeavour let story-driven reporters go nuts: they were allegedly waiting for the innovation outside his laboratory for weeks in vain. Some went as far as to fabricate images about the human brain. One of them turned out to be a pan of cat intestines radiographed in 1896 by H. A. Falk!\u003C\u002Fp>\n\n\n\n\u003Cp>While some early efforts turned out to be huge blows and impossible projects, X-rays got acclimatised in medicine. Something similar will happen with A.I. and healthcare soon. I hope with fewer cat intestines, though.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Radiology has been the playfield of technological development since the beginnings\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>In the TV series, \u003Cem>The Knick\u003C\u002Fem> depicting the first decades of modern surgery and healthcare, an inventor gets in touch with the hospital manager in his office to present him with a new idea, the X-ray machine. It turns out that it takes an hour or so for the brand-new machine to take the picture! Currently, if you go to the hospital to get the annual check-up on your lungs done, the X-ray procedure will take a couple of minutes in a fortunate situation, and some more until you get the results.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\">\u003Cdiv class=\"wp-block-embed__wrapper\">\n\u003Ciframe loading=\"lazy\" title=\"The Knick S01E06 X ray scene 2 1\" width=\"640\" height=\"360\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FU7XOYZsnTxM?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>Plenty has changed since those experiments with the ‘X-ray lamp’, but one thing was constant: rapid technological development in radiology.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>A bigger range of tools and higher precision\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Approximately half a century after the discovery of the X-ray, ultrasound joined the methods of medical imaging. From the mid-sixties onwards, the advent of commercially available systems allowed wider dissemination. Rapid technological advances in electronics and piezoelectric materials provided further improvements from bistable to greyscale images and from still images to real-time moving images. And it is also amazing to see how we went from room-sized, clumsy ultrasound machines to portable ones circa within another half of a century! In 2016, Clarius Mobile Health introduced the world’s first handheld ultrasound scanner with a mobile application. The doctor can carry around the personal ultrasound device for quick exams and to guide procedures such as nerve blocks and targeted injections.\u003C\u002Fp>\n\n\n\n\u003Cp>Now, let’s look at body scanners. The first CT scanners were\u003Ca href=\"https:\u002F\u002Fradiopaedia.org\u002Farticles\u002Fct-scanner-evolution\"> introduced in 1971 with a single detector for brain study under the leadership of Godfrey Hounsfield\u003C\u002Fa>, an electrical engineer at EMI (Electric and Musical Industries, Ltd). The very first \u003Ca href=\"http:\u002F\u002Fwww.two-views.com\u002Fmri-imaging\u002Fhistory.html#sthash.RbPBZETW.dpbs\">MRI scanner was built by Raymond Damadian in the 1970s by hand\u003C\u002Fa>, assisted by his students at New York’s Downstate Medical Center. He achieved the first MRI scan of a healthy human body in 1977 and a human organism with cancer in 1978. The first functional MR imaging of the human brain is produced in the early 1990s. By the early 2000s, cardiac MRI, body MRI, fetal imaging, and functional MR imaging became routine exams in many imaging centers.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"870\" height=\"785\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fmedical-imaging-techniques.jpg\" alt=\"Future of Radiology\" class=\"wp-image-15912\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fmedical-imaging-techniques.jpg 870w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fmedical-imaging-techniques-768x693.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fmedical-imaging-techniques-512x462.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fmedical-imaging-techniques-277x250.jpg 277w\" sizes=\"auto, (max-width: 870px) 100vw, 870px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003Cstrong>Along with precision comes automation \u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Thus, the history of radiology shows the expansion of means as well as the increase in precision so far. While the latter is still in focus, there is also a visible shift towards making radiologists’ lives easier by automation. As radiologists need to go through more and more images every day, it becomes inevitable that part of their job can be automated. When we can train algorithms to spot and detect many types of abnormalities based on radiology images, why wouldn’t we let it do the time-consuming job so we can let radiologists dedicate their precious focus to the hardest issues?\u003C\u002Fp>\n\n\n\n\u003Cp>When deep learning becomes possible and the algorithm could teach itself while radiologists rate its effectiveness, it’s going to get better just by working more. This is an opportunity we have to grab. This way radiology would be one of the most creative specialities in which problem-solving and a holistic approach would be the key.\u003C\u002Fp>\n\n\n\n\u003Cp>So, it certainly would not mean that A.I. would take over all the tasks of radiologists. As Erickson put it, if you look at the frequency of findings and diagnoses on medical images\u003Cem>, there are the common ones where AI\u003C\u002Fem> \u003Cem>could help, but there is a really long tail, uncommon but really important things that we cannot miss\u003C\u002Fem>. He believes that it is going to be difficult for deep learning algorithms to identify those. But where do we stand with technology at the moment?\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Could AI\u003C\u002Fstrong> \u003Cstrong>predict whether you would die soon?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Scientists at the University of Adelaide have been experimenting with an \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-017-01931-w#Fig4\" target=\"_blank\">AI system that is said to be able to tell if you are going to die\u003C\u002Fa>. By analysing CT scans from 48 patients, the deep learning algorithms could predict whether they’d die within five years with 69 percent accuracy.  It is “broadly similar” to scores from human diagnosticians, the paper says. It is an impressive achievement. The deep learning system was trained to analyse over 16,000 image features that could indicate signs of disease in those organs. Researchers say that their goal is for the algorithm to measure overall health rather than spot a single disease.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"660\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F03\u002Ffda-approvals-ai-algorithms-768x660.png\" alt=\"FDA-approved AI-based algorithms\" class=\"wp-image-33293\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F03\u002Ffda-approvals-ai-algorithms-768x660.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F03\u002Ffda-approvals-ai-algorithms.png 1257w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">To view the infographics in full size, right click and open in a new browser tab\u002Fwindow\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Cp>This is just one of the numerous initiatives on developing artificial intelligence applications to support the field of radiology. You can take a look at this article published by The Medical Futurist Institute in \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41746-020-00324-0\" target=\"_blank\">npj Digital Medicine journal\u003C\u002Fa>, or \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffda-approved-ai-based-algorithms\u002F\" target=\"_blank\">the online database\u003C\u002Fa> we keep updating ever since. It currently has 79 entries, of which 39 belong to the field of radiology. That is undisputedly the medical field with the highest number of AI initiatives. \u003C\u002Fp>\n\n\n\n\u003Cp>However, the ongoing research does not mean that we are already at the stage where average patients will have to face their exact life expectancy based on their medical images when they go to the hospital.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>What are the challenges in introducing AI\u003C\u002Fstrong> \u003Cstrong>to the radiology department?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>In order to have some estimation of when machine learning might be introduced on a wider scale, we have to look at how machine learning takes place in radiology. The process usually goes like this: the algorithm should be fed by thousands, if not millions of images and learn to spot differences regarding tissues. Just as in the case of computers recognising images of dogs and cats. If the algorithm makes a mistake, the researcher notices it and adjusts the code. Thus, it is a rather lengthy process that needs tons of available data. Erickson believes that the result will look like the following: we’ll do the high volume exam, and the algorithm will probably create a structured, minable, preliminary report. \u003Cem>So it will do the quantification that most humans hate to do and it will do that very well\u003C\u002Fem>, he noted.\u003C\u002Fp>\n\n\n\n\u003Cp>Anna Fernandez, Health Informatics\u002FPrecision Medicine Lead at Booz Allen Hamilton told me though that \u003Cem>there are several challenges in building these discovery and analytic platforms – from acquiring access and ingesting the data, sufficiently annotating the data, storage strategy, governance\u002Fpolicy use throughout, and types of analysis enabled via the platform.\u003C\u002Fem> The biggest challenge is sufficiently annotating the data to allow different views of it (full right to owners, restricted subset to others) and enable discovery across the connected data sets in the platform.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"870\" height=\"580\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fai-and-radiology.jpg\" alt=\"Future of Radiology\" class=\"wp-image-15917\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fai-and-radiology.jpg 870w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fai-and-radiology-768x512.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fai-and-radiology-512x341.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Fai-and-radiology-375x250.jpg 375w\" sizes=\"auto, (max-width: 870px) 100vw, 870px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Moreover, hospitals also need to be convinced that A.I. algorithms work. Fernandez believes that it will be a step-wise process by for example taking advantage of hybrid internal and external “crowdsourcing” with sufficiently anonymised data.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cem>For example, a vendor can have established data science algorithms based on anonymised data from their hospital network, then the new hospital can employ the algorithm and further refine it to the anonymised “local” data sets (that may include additional patient variables) to customise it to their population.\u003C\u002Fem> As the hospitals see a “win,” they may be encouraged to release a more restricted anonymised data set to contribute back to the vendor solution. So it’s a little bit similar to how you try to go into the cold water on a hot summer day. First, you look at other people doing it, then you realise it’s safe, so you put your toes in the water before entirely going under.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>We get to have AI analyzing our CT scans\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>How can we depict in a concise and easy-to-grasp way how the human-A.I. collaboration will unfold in the field of medicine in the years and decades to come? Andrew Ng, founder of \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.deeplearning.ai\u002F\" target=\"_blank\">deeplearning.ai\u003C\u002Fa> described five levels of automation. The Medical Futurist implemented this concept in medicine, and explained these levels with current examples and future scenarios \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002F5-levels-of-automation-in-medicine\u002F\" target=\"_blank\">in this article\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>Below is such an infographic that helps in visualising the spectrum of automation in medicine, ranging from human-only (level1) to fully automated (level5).\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large is-style-default\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"1292\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ftmf-5-levels-of-automation-768x1292.png\" alt=\"\" class=\"wp-image-47253\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ftmf-5-levels-of-automation-768x1292.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ftmf-5-levels-of-automation.png 642w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Future radiology is expected to work with level 3 (AI assistance) and level 4 (Partial automation) algorithms. What do these mean? \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>At the third level\u003C\u002Fstrong>, the AI system supports physicians in clinical decision-making via suggestions. For example, after scanning a database of chest CT scans, the A.I. considers the chest CT results of a patient being investigated and highlights suspicious signs. These signs are then further investigated by the physician. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>On level four\u003C\u002Fstrong>, with partial automation, an AI system can come up with its own diagnosis; but if it’s not confident enough about it, the AI turns to physicians for help. Several companies are working on such solutions today. A \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fbeholdai.wordpress.com\u002F2022\u002F08\u002F10\u002Fsouthend_backlog\u002F\" target=\"_blank\">practical example\u003C\u002Fa> is Behold’s Class IIa CE marked platform that is used by UK’s NHS Trust to help clear the radiology backlogs in lung cancer screening. It can process adult frontal Chest X-Ray examinations and has two key outputs. It either flags the image as “suspected lung cancer” and prioritises the patient for a radiologist consultation, or it identifies the image as normal – although the image will also be audited by a radiologist.\u003C\u002Fp>\n\n\n\n\u003Cp>Palo Alto-based Nines \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedcitynews.com\u002F2020\u002F04\u002Fnines-gets-fda-clearance-for-ai-to-flag-two-life-threatening-conditions\u002F?rf=1\" target=\"_blank\">developed an AI-system\u003C\u002Fa> that can identify potential cases of intracranial haemorrhage and mass effect from CT scans. It then flags those cases for radiologists to review.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Radiology’s Future is AI\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>All in all, research trends and experts underline how AI will revolutionise radiology in the long term. Thus, rather than neglecting it or feeling threatened by it, the medical community should embrace its achievements.\u003C\u002Fp>\n\n\n\n\u003Ch4 class=\"wp-block-heading\">\u003Cstrong>Yes, it is possible that a big chunk of the tasks radiologists do today will be automated, covering all repetitive, data-based tasks. It will free up capacities for more meaningful assignments, and the AI-based technologies themselves will be designed and controlled by radiologists.   \u003C\u002Fstrong>\u003C\u002Fh4>\n\n\n\n\u003Cp>As Erickson put it, rather than pushing off machine intelligence as being a threat to their job, instead, radiologists should engage it, because it’s something that can really help patients. I’m sure it will dramatically change what radiologists\u003Cem> will do over the next ten years, but you should also keep in mind that eventually, radiology ten years ago was nothing like what it is today.\u003C\u002Fem> So it is just one of those things where we need to make sure that we keep at the forefront; that we keep in mind that what matters most is taking care of patients. I could not agree more and could not express it better. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Learn more about the technological future of medical specialties from \u003Ca href=\"https:\u002F\u002Fleanpub.com\u002Ffuture-of-medical-specialties\">our latest e-book\u003C\u002Fa>!\u003C\u002Fh3>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002F0415_tmfs_mockup.png\" alt=\"future of medical specialties\" class=\"wp-image-23819\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002F0415_tmfs_mockup.png 1920w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002F0415_tmfs_mockup-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002F0415_tmfs_mockup-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002F0415_tmfs_mockup-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F05\u002F0415_tmfs_mockup-2048x1152.png 2048w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \u002F>\u003C\u002Ffigure>\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":167,"protected":20},"\u003Cp>Radiologists’ creative work will be necessary in the future to solve complex issues and supervising diagnostic processes; but AI will definitely become part of their daily routine in diagnosing simpler cases and taking over repetitive tasks. \u003C\u002Fp>\n",15922,{"_acf_changed":20,"footnotes":27},[171],521,[173,174,175,176,177,178,179,180,181,182,183,184,185,186,187],246,628,271,708,275,282,289,313,327,372,134,425,144,519,163,[189,34,190],949,953,[],[193,194,45,195,196,197,38,198],1883,2739,1723,1729,1783,1833,[200,14,48,49,50,51,52,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,54,218],"post-15909","category-future-medicine","tag-future","tag-medical-imaging","tag-health","tag-ct-scanning","tag-healthcare","tag-ibm-watson","tag-innovation","tag-medicine","tag-mri","tag-radiology","tag-ai","tag-technology-2","tag-artificial-intelligence","tag-gc4","tag-cancer-2","project_category-educators","project_category-researchers",{"id":168,"alt_text":220,"caption":27,"description":27,"media_type":57,"media_details":221,"post":96,"source_url":253},"Future of Radiology",{"width":222,"height":223,"file":224,"sizes":225,"image_meta":251},870,532,"2017\u002F06\u002Ffuture-of-radiology-1.jpg",{"medium":226,"large":232,"thumbnail":237,"medium_large":241,"large_old_512x313":242,"medium_old_409x250":246},{"file":227,"width":228,"height":229,"mime-type":230,"source_url":231},"future-of-radiology-1-370x208.jpg",370,208,"image\u002Fjpeg","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ffuture-of-radiology-1-370x208.jpg",{"file":233,"width":234,"height":235,"mime-type":230,"source_url":236},"future-of-radiology-1-768x470.jpg",768,470,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ffuture-of-radiology-1-768x470.jpg",{"file":238,"width":239,"height":239,"mime-type":230,"source_url":240},"future-of-radiology-1-150x150.jpg",150,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ffuture-of-radiology-1-150x150.jpg",{"file":233,"width":234,"height":235,"mime-type":230,"source_url":236},{"file":243,"width":244,"height":180,"mime-type":230,"source_url":245},"future-of-radiology-1-512x313.jpg",512,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ffuture-of-radiology-1-512x313.jpg",{"file":247,"width":248,"height":249,"mime-type":230,"source_url":250},"future-of-radiology-1-409x250.jpg",409,250,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ffuture-of-radiology-1-409x250.jpg",{"aperture":85,"credit":27,"camera":27,"caption":27,"created_timestamp":85,"copyright":27,"focal_length":85,"iso":85,"shutter_speed":85,"title":27,"orientation":85,"keywords":252},[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F06\u002Ffuture-of-radiology-1.jpg",{"related_posts":20,"related_posts_footer":20,"cta_type":27,"cta_color":27,"subtitle":27,"related_books":20,"key_takeaways":255},[256,258,260],{"title":257},"\u003Cp>What if an algorithm could tell you whether you have cancer based on your CT scan or mammography exam?\u003C\u002Fp>\n",{"title":259},"\u003Cp>Radiologists’ creative work will be necessary in the future to solve complex issues and supervising diagnostic processes; but AI will definitely become part of their daily routine in diagnosing simpler cases and taking over repetitive tasks.\u003C\u002Fp>\n",{"title":261},"\u003Cp>Rather than getting threatened by it, we should familiarize with how it could help change the course of radiology for the better.\u003C\u002Fp>\n",{"yoast_wpseo_title":263,"yoast_wpseo_metadesc":264,"yoast_wpseo_canonical":161},"The Future of Radiology And Artificial Intelligence - The Medical Futurist","AI will become part of the daily routine of radiologists soon. So rather than getting threatened, we should understand how it changes its future.",{"self":266,"collection":271,"about":273,"author":275,"replies":277,"version-history":280,"predecessor-version":284,"wp:featuredmedia":288,"wp:attachment":291,"wp:term":294,"curies":305},[267],{"href":268,"targetHints":269},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F15909",{"allow":270},[105],[272],{"href":108},[274],{"href":111},[276],{"embeddable":26,"href":114},[278],{"embeddable":26,"href":279},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=15909",[281],{"count":282,"href":283},47,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F15909\u002Frevisions",[285],{"id":286,"href":287},59289,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F15909\u002Frevisions\u002F59289",[289],{"embeddable":26,"href":290},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F15922",[292],{"href":293},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=15909",[295,297,299,301,303],{"taxonomy":134,"embeddable":26,"href":296},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=15909",{"taxonomy":137,"embeddable":26,"href":298},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=15909",{"taxonomy":140,"embeddable":26,"href":300},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=15909",{"taxonomy":143,"embeddable":26,"href":302},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=15909",{"taxonomy":146,"embeddable":26,"href":304},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=15909",[306],{"name":150,"href":151,"templated":26},[308],{"id":92,"date":309,"date_gmt":310,"guid":311,"modified":313,"modified_gmt":314,"slug":315,"status":13,"type":316,"link":317,"title":318,"content":320,"excerpt":322,"author":23,"featured_media":324,"comment_status":25,"ping_status":25,"template":27,"yst_prominent_words":325,"class_list":329,"better_featured_image":332,"acf":350,"yoast_meta":372,"_links":375},"2020-10-01T10:54:54","2020-10-01T08:54:54",{"rendered":312},"https:\u002F\u002Fapi.medicalfuturist.com\u002F?post_type=book&p=30419","2023-03-12T18:16:29","2023-03-12T17:16:29","privacy-in-digital-health","book","https:\u002F\u002Fapi.medicalfuturist.com\u002Fbooks\u002Fprivacy-in-digital-health\u002F",{"rendered":319},"Hackers, Breaches and the Value of Health Data",{"rendered":321,"protected":20},"\n\u003Cp>Today, everyone needs to understand that there is no digital health without sacrificing a part of our privacy. The advanced technologies fueling the transformation cannot improve without our data; and without it, they can’t be implemented as part of regular medical care. And COVID-19 has only made things worse.\u003C\u002Fp>\n\n\n\n\u003Cp>In this e-Book, we defined the three cornerstones of privacy of every privacy discussion going forward: the traditional, the new and the future spheres that deal with your health data, and put forward recommendations on how you can start protecting yourself.\u003C\u002Fp>\n",{"rendered":323,"protected":20},"\u003Cp>Today, everyone needs to understand that there is no digital health without sacrificing a part of our privacy. The advanced technologies fueling the transformation cannot […]\u003C\u002Fp>\n",49967,[326,327,328],1693,1571,3633,[330,316,331,49,51,52],"post-30419","type-book",{"id":324,"alt_text":27,"caption":27,"description":27,"media_type":57,"media_details":333,"post":92,"source_url":349},{"width":334,"height":335,"file":336,"filesize":337,"sizes":338,"image_meta":347},320,414,"2020\u002F10\u002Fhack-breaches-health-data.png",89249,{"medium":339,"thumbnail":343},{"file":340,"width":334,"height":229,"mime-type":67,"filesize":341,"source_url":342},"hack-breaches-health-data-320x208.png",53801,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002Fhack-breaches-health-data-320x208.png",{"file":344,"width":239,"height":239,"mime-type":67,"filesize":345,"source_url":346},"hack-breaches-health-data-150x150.png",20987,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002Fhack-breaches-health-data-150x150.png",{"aperture":85,"credit":27,"camera":27,"caption":27,"created_timestamp":85,"copyright":27,"focal_length":85,"iso":85,"shutter_speed":85,"title":27,"orientation":85,"keywords":348},[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002Fhack-breaches-health-data.png",{"buy_button_text":351,"leanpub_url":352,"preview":353},"Get it on Leanpub","https:\u002F\u002Fleanpub.com\u002Fprivacy-in-digital-health\u002F",[354,356,358,360,362,364,366,368,370],{"image":355},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-01.png",{"image":357},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-03.png",{"image":359},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-04.png",{"image":361},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-05.png",{"image":363},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-06.png",{"image":365},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-07.png",{"image":367},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-08.png",{"image":369},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-09.png",{"image":371},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002F0930_PRIVACY_TMF_EBOOK-10.png",{"yoast_wpseo_title":373,"yoast_wpseo_metadesc":374,"yoast_wpseo_canonical":317},"Hackers, Breaches and the Value of Health Data: What You Need To Know - The Medical Futurist","Privacy in Digital Health: Privacy and security issues pertaining to the digital health era are complex and multifactorial. Learn more from our book",{"self":376,"collection":381,"about":384,"author":387,"replies":389,"wp:featuredmedia":392,"wp:attachment":395,"wp:term":398,"curies":401},[377],{"href":378,"targetHints":379},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fbook\u002F30419",{"allow":380},[105],[382],{"href":383},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fbook",[385],{"href":386},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fbook",[388],{"embeddable":26,"href":114},[390],{"embeddable":26,"href":391},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=30419",[393],{"embeddable":26,"href":394},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F49967",[396],{"href":397},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=30419",[399],{"taxonomy":146,"embeddable":26,"href":400},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=30419",[402],{"name":150,"href":151,"templated":26},1788949169045]