[{"data":1,"prerenderedAt":322},["ShallowReactive",2],{"slug-top-ai-algorithms-healthcare":3},{"post":4,"relatedPosts":181,"relatedBooks":321},{"id":5,"date":6,"date_gmt":7,"guid":8,"modified":6,"modified_gmt":7,"slug":10,"status":11,"type":12,"link":13,"title":14,"content":16,"excerpt":19,"author":21,"featured_media":22,"comment_status":23,"ping_status":23,"sticky":24,"template":25,"format":26,"meta":27,"categories":28,"tags":30,"project_category":43,"contact_email_category":45,"yst_prominent_words":46,"class_list":58,"better_featured_image":79,"acf":111,"yoast_meta":125,"_links":128},22987,"2025-11-27T13:33:46","2025-11-27T12:33:46",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=22987&_wpnonce=faf4ca9c84&status=auto-draft&type=post","top-ai-algorithms-healthcare","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Ftop-ai-algorithms-healthcare",{"rendered":15},"Top Smart Algorithms In Healthcare",{"rendered":17,"protected":18},"\n\u003Cp>As artificial intelligence (AI) tools have been invading more or less every area of healthcare, we made a list to keep track of the top AI algorithms aiming for better diagnostics, more sophisticated patient care or further sighted predictions of diseases.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Does AI beat doctors?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Only if you have lived under a rock for the last couple of years could you not have heard about artificial intelligence and the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-will-redesign-healthcare\" target=\"_blank\">potential of AI in healthcare\u003C\u002Fa>. \u003Cstrong>Not only smart algorithms themselves but also the \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fwhat-does-gpt-4-mean-for-healthcare\" target=\"_blank\" rel=\"noreferrer noopener\">hype around AI\u003C\u002Fa> has grown immensely, thus every time a new study about deep learning or machine learning in diagnostics, medical imaging or any other medical field gets published, newsreaders can be sure that some titles will say that “\u003Cem>\u003Ca href=\"https:\u002F\u002Fcardiovascularbusiness.com\u002Ftopics\u002Fcardiac-imaging\u002Fechocardiography\u002Fcardiologists-find-ai-be-more-accurate-sonographers-interpreting-echocardiograms\" target=\"_blank\" rel=\"noreferrer noopener\">AI\u003C\u002Fa> has \u003Ca href=\"https:\u002F\u002Fwww.medpagetoday.com\u002Fspecial-reports\u002Fexclusives\u002F103522\" target=\"_blank\" rel=\"noreferrer noopener\">again \u003C\u002Fa>beaten \u003Ca href=\"https:\u002F\u002Fwww.bbc.com\u002Fnews\u002Fhealth-50857759\" target=\"_blank\" rel=\"noreferrer noopener\">doctors\u003C\u002Fa> in \u003Ca href=\"https:\u002F\u002Fwww.insideprecisionmedicine.com\u002Ftopics\u002Fpatient-care\u002Fear-disorders\u002Fai-better-than-doctors-at-diagnosing-pediatric-ear-infections\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">field X\u003C\u002Fa>\u003C\u002Fem>”\u003C\u002Fstrong>. \u003C\u002Fp>\n\n\n\n\u003Cp>The narrative is so distorted towards extreme visions that artificial intelligence is either presented as the ultimate evil destroying mankind – both \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=B-Osn1gMNtw\" target=\"_blank\">Elon Musk\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"http:\u002F\u002Fwww.bbc.com\u002Fnews\u002Ftechnology-30290540\" target=\"_blank\">Stephen Hawking\u003C\u002Fa> warned about that years ago – or the only source of the future prosperity of humanity. \u003C\u002Fp>\n\n\n\n\u003Cp>Just think of the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ftechcrunch.com\u002F2023\u002F03\u002F28\u002F1100-notable-signatories-just-signed-an-open-letter-asking-all-ai-labs-to-immediately-pause-for-at-least-6-months\u002F?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAKmu7s0KQrRwyPz-pZWYbtBvxSfTZel6Bhu79sE47N0ZiTHa8J8dgD0cpbPzxWkUDxqHzo4b8Kmm_vdp9C8AksGAcAa1tGtZTulyeb_ErIP9rg_WbwQYPX1diD78X2bwWJ6Q9jYS_j0UWxGpe-Ryaz9gaJfohEA_9UcTtCvGpK3n\" target=\"_blank\">open letter conundrum\u003C\u002Fa> of \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-023-00649-x\" target=\"_blank\">recent weeks\u003C\u002Fa>, and how fears of the general population were fueled despite the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fventurebeat.com\u002Fai\u002Ftitans-of-ai-industry-andrew-ng-and-yann-lecun-oppose-call-for-pause-on-powerful-ai-systems\u002F\" target=\"_blank\">opposing opinions\u003C\u002Fa> of the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.reuters.com\u002Ftechnology\u002Fbill-gates-says-calls-pause-ai-wont-solve-challenges-2023-04-04\u002F\" target=\"_blank\">largest minds\u003C\u002Fa> calling for a more nuanced approach. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>By enumerating the top AI tools that we discovered in healthcare so far, we also aim to add what we believe is already useful for the work of medical professionals.\u003C\u002Fstrong> \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full is-resized\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F04\u002Ftmf_article_357.png\" alt=\"TMF, medical student, AI, doctor, data, computer\" class=\"wp-image-50547\" style=\"width:960px;height:540px\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F04\u002Ftmf_article_357.png 1920w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F04\u002Ftmf_article_357-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F04\u002Ftmf_article_357-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F04\u002Ftmf_article_357-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F04\u002Ftmf_article_357-2048x1152.png 2048w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Life is no training data set\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Artificial intelligence \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.fastcompany.com\u002F90863983\u002Fchatgpt-medical-diagnosis-emergency-room?utm_source=The+Medical+Futurist+Newsletter&utm_campaign=872032dc60-EMAIL_CAMPAIGN_2022_02_01_COPY_01&utm_medium=email&utm_term=0_efd6a3cd08-872032dc60-420889096&mc_cid=872032dc60&mc_eid=b5d5d75ed3\" target=\"_blank\">has numerous limitations\u003C\u002Fa> just as yet, so before we present our list, it’s worth going through them one by one. Already the term is misleading as AI implies a far more developed technology where it is standing at the moment. At best, current science – meaning \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fmedpalm-new-ai-medical-chatbots-will-soon-be-better-than-waiting-for-a-doctor\" target=\"_blank\" rel=\"noreferrer noopener\">large language models\u003C\u002Fa> and various \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fgenerative-adversarial-networks-how-can-ai-learn-so-much-while-its-iq-remains-zero\" target=\"_blank\" rel=\"noreferrer noopener\">machine learning methods\u003C\u002Fa> – is able to reach artificial narrow intelligence (ANI) in multiple fields, the first level of intelligence created by humans. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Yet, we have \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.deeplearning.ai\u002Fthe-batch\u002Fartificial-general-intelligence-progress-report\u002F\" target=\"_blank\">not arrived at artificial general intelligence\u003C\u002Fa> (AGI)\u003C\u002Fstrong>, the second level of intelligence when a machine is capable of abstracting concepts from limited experience and transferring knowledge between domains. The third and most fearful domain, superintelligence, when AI evolves into a stand-alone consciousness, is nowhere close.\u003C\u002Fp>\n\n\n\n\u003Cp>Yet, ANI and its two main streams, natural language processing, and computer vision, are developing at an incredible speed. The latter is crucial for diagnostics in healthcare as it is based on pattern recognition. Countless algorithms are currently trained to categorize various patterns seen in medical images and thus help doctors diagnose conditions. \u003C\u002Fp>\n\n\n\n\u003Cp>The limitations of such studies are present in at least three areas. At first, used medical evidence tends to originate from highly developed regions containing their specificity or the framework for conceptualizing the algorithm itself incorporates the subjective assumptions of the working team. Secondly, the forecasting and predictive abilities of smart algorithms are anchored in previous cases – however, they might be useless in new cases of drug side effects or treatment resistance.\u003C\u002Fp>\n\n\n\n\u003Cp>Finally, the majority of the already conducted AI research has been done on training data sets collected from various medical facilities and after the algorithm analyses the images, doctors are provided with the same dataset – usually without reproducing the clinical conditions. \u003C\u002Fp>\n\n\n\n\u003Cp>That does not decrease the theoretical value of the study – but its practical implementation. Life is no training data set. Thousands of patients come and go to a hospital with thousands of symptoms and describe similar or the same conditions very differently. Thus, the results of AI studies conducted on training data sets might not be representative of what would happen in real-life situations. Common interpretations often leave out these limitations focusing on presenting these studies as ultimate revelations.\u003C\u002Fp>\n\n\n\n\u003Cp>Keeping all these constraints in mind, here are the top AI algorithms that we recently found in healthcare.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">1) The algorithm spotting DNA mutations in tumors\u003C\u002Fh2>\n\n\n\n\u003Cp>One of the reasons why it’s so incredibly difficult to treat cancer is that malignant tumors tend to mutate, grow, evolve and change. In the last years, scientists discovered that not only cancer itself transforms but so does its DNA. As sequencing costs significantly dropped, the genetic analysis of tumors became possible, and recently, \u003Ca href=\"https:\u002F\u002Fspectrum.ieee.org\u002Fthe-human-os\u002Fbiomedical\u002Fdiagnostics\u002Fmachine-learning-tool-can-spot-mutations-in-tumors\" target=\"_blank\" rel=\"noreferrer noopener\">human experts with the support of computational tools started to analyze the data\u003C\u002Fa> to figure out what kinds of genetic changes, or mutations, occur.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full is-resized\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3200\" height=\"1800\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology.png\" alt=\"creativity in healthcare, TMF\" class=\"wp-image-22989\" style=\"width:800px;height:450px\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology.png 3200w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002FFuture-of-pathology-512x288.png 512w\" sizes=\"auto, (max-width: 3200px) 100vw, 3200px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>For making such existing tools more precise, Personal Genome Diagnostics in Baltimore developed a new method involving machine learning that automates the tumor DNA diagnostic process and improves the accuracy of identifying mutations in cancerous tissues. Bearing that result in mind, the doctor can choose the specific targeted treatment for the patient.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">2) Can AI score better in classifying heart images than humans?\u003C\u002Fh2>\n\n\n\n\u003Cp>Echocardiograms produce sound waves to paint the heart’s picture – from which cardiologists can identify whether the patient has any heart disease. It’s a standard test to check for problems with valves or chambers of our central organ, for congenital heart defect or whether shortness of breath or chest pain is in connection with the heart. \u003C\u002Fp>\n\n\n\n\u003Cp>A fascinating development in this field \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-023-05947-3\" target=\"_blank\">came from the researchers of Cedars Sinai’s Smidt Heart Institute\u003C\u002Fa> and Division of Artificial Intelligence in Medicine. They reported that artificial intelligence (AI) proved more successful in assessing and diagnosing cardiac function when compared to echocardiogram assessments made by sonographers. In this first randomized, blinded trial cardiologists evaluated 3,495 transthoracic echocardiogram studies, comparing initial assessment by artificial intelligence or by a sonographer. One of the major findings was that cardiologists more frequently agreed with the AI initial assessment, such that they corrected only 16.8% of the initial assessments made by AI and simultaneously corrected 27.2% of the initial assessments made by the sonographers.\u003C\u002Fp>\n\n\n\n\u003Cp>Of course, this was not the first initiative to use AI in cardiology. Earlier Rima Arnaut, an assistant professor and practising cardiologist at UC San Francisco and her colleagues used \u003Ca href=\"https:\u002F\u002Fspectrum.ieee.org\u002Fthe-human-os\u002Fbiomedical\u002Fdiagnostics\u002Fai-cardiologist-aces-its-first-medical-exam\">deep learning to train an AI system that can classify echocardiograms\u003C\u002Fa> according to the type of view shown. When both the AI and expert cardiologists were asked to sort the images, the algorithm achieved an accuracy of 92 percent. The humans got only 79 percent correct. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">3) Heart attack predicting algorithms\u003C\u002Fh2>\n\n\n\n\u003Cp>Smart algorithms do not only outperform doctors when it comes to classifying but also in predicting outcomes based on various factors. \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fspectrum.ieee.org\u002Fthe-human-os\u002Fbiomedical\u002Fdiagnostics\u002Fai-predicts-heart-attacks-more-accurately-than-standard-doctor-method\" target=\"_blank\">Researchers at the University of Nottingham in the UK created a system\u003C\u002Fa> that scanned patients’ routine medical data and predicted which of them would have heart attacks or strokes within 10 years. When compared to the standard method of prediction based on well-established risk factors such as high blood pressure, cholesterol, age, smoking, and diabetes, the AI system correctly predicted the fates of 355 more patients.\u003C\u002Fp>\n\n\n\n\u003Cp>There are many promising initiatives in this field, some aim \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.news-medical.net\u002Fnews\u002F20220517\u002FUsing-AI-to-predict-heart-attacks.aspx\" target=\"_blank\">to predict heart attacks\u003C\u002Fa> years \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fedition.cnn.com\u002F2022\u002F11\u002F29\u002Fhealth\u002Fheart-attack-stroke-x-ray\u002Findex.html\" target=\"_blank\">before they actually occurred\u003C\u002Fa>, another distinguishes \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.cedars-sinai.org\u002Fnewsroom\u002Fpredicting-sudden-cardiac-arrest\u002F\" target=\"_blank\">between treatable and untreatable\u003C\u002Fa> sudden cardiac arrests, while this algorithm helps to \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.bhf.org.uk\u002Fwhat-we-do\u002Fnews-from-the-bhf\u002Fnews-archive\u002F2022\u002Faugust\u002Fartificial-intelligence-could-help-narrow-heart-attack-gender-gap\" target=\"_blank\">more accurately detect heart attacks in women\u003C\u002Fa>, whose condition often remains undetected under standard circumstances. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">4) More precise skin cancer diagnoses with AI\u003C\u002Fh2>\n\n\n\n\u003Cp>According to \u003Ca href=\"https:\u002F\u002Fwww.iarc.who.int\u002Fcancer-type\u002Fskin-cancer\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">statistics from the WHO\u003C\u002Fa>, currently, around 1.5 million non-melanoma skin cancers and 325,000 melanoma skin cancers occur each year globally. Digital health technologies, such as\u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-2023-skin-checking-apps-landscape-infographic\" target=\"_blank\" rel=\"noreferrer noopener\"> smartphone apps\u003C\u002Fa> like \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Faspiring-dermatology-app-under-the-microscope-the-skinvision-review\" target=\"_blank\">SkinVision\u003C\u002Fa>, telemedical services as well as AI are at the frontlines of fighting the widely prevalent disease.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\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 class=\"wp-element-caption\">Skin Checking Algorithms\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Although several research groups developed smart algorithms for diagnosing skin cancer already, the \u003Ca href=\"https:\u002F\u002Fspectrum.ieee.org\u002Fthe-human-os\u002Fbiomedical\u002Fdiagnostics\u002Fcomputer-diagnoses-skin-cancers\" target=\"_blank\" rel=\"noreferrer noopener\">one created at Stanford University is likely the most robust system\u003C\u002Fa> so far. It was trained on more than 1.28 million images and fine-tuned with a set of nearly 130,000 scans of skin lesions from more than 2000 diseases. That’s the most extensive dataset used for automated skin cancer classification as of yet. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">5) AI systems for the ICU\u003C\u002Fh2>\n\n\n\n\u003Cp>Intensive care units are battlegrounds for human lives. As every moment counts, patients are monitored 24\u002F7 with an army of devices. Constantly beeping bedside monitors show blood pressure, heart rate or any other vital signs of the patient, a machine takes care of the function of the lungs as best as possible, and another goes for the heart. However, these instruments are usually not connected, they are isolated units in the concert of ICU care.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">6) AI detecting breast cancer\u003C\u002Fh2>\n\n\n\n\u003Cp>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. In spite of global awareness-raising and prevention efforts, there were \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.wcrf.org\u002Fdietandcancer\u002Fcancer-trends\u002Fbreast-cancer-statistics\" target=\"_blank\">over 2 million new cases in 2020\u003C\u002Fa>. The statistics also show that almost 700,000 women died from breast cancer in 2020.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"433\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002F070_womens_health-scaled-768x433.png\" alt=\"Women and digital health\" class=\"wp-image-23124\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002F070_womens_health-scaled-768x433.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002F070_womens_health-scaled-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002F070_womens_health-scaled-1536x866.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002F070_womens_health-scaled.png 1915w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>As in the case of many other cancer types, early detection could be a lifesaver. However, women with dense breasts have a higher risk of undergoing mammogram screenings that miss signs of breast cancer. Researchers at the University of California, San Francisco found that commercial software for automatically classifying breast density and thus detecting breast cancer is just as accurate as human radiologists. Shortly, the algorithm could support doctors with cases when breast density would not allow clear diagnosis.\u003C\u002Fp>\n\n\n\n\u003Cp>Algorithms could not only assist radiologists but also pathologists in their fight against breast cancer. The \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.05718\" target=\"_blank\" rel=\"noreferrer noopener\">International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems\u003C\u002Fa> for the automated detection of metastatic breast cancer. The winning study showed that combining the efforts of the human pathologist and the deep learning system’s predictions, the human error rate decreased by 85 percent when identifying metastatic breast cancer.\u003C\u002Fp>\n\n\n\n\u003Cp>That’s an impressive result, especially bearing in mind that early diagnosis means saving lives when it comes to the lethal disease. Beyond the achievement, it is worth noting that the joint efforts of AI and human doctors indicated a significant improvement in diagnosing, their single results were not even close.\u003C\u002Fp>\n\n\n\n\u003Cp>AI is \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.nytimes.com\u002F2023\u002F03\u002F05\u002Ftechnology\u002Fartificial-intelligence-breast-cancer-detection.html\" target=\"_blank\">already used in Hungary\u003C\u002Fa>, where at five hospitals and clinics that perform more than 35,000 screenings a year, AI systems were rolled out starting in 2021 and now help to check for signs of cancer that a radiologist may have overlooked.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">7) Smart algorithm predicting suicide risk\u003C\u002Fh2>\n\n\n\n\u003Cp>In the future, you might go to the hospital with a broken arm and leave the facility with a cast and a note with a compulsory psychiatry session due to flagged suicide risk. That’s what some scientists aim for with their AI system developed to catch depressive behaviour early on and help reduce the emergence of severe mental illnesses.\u003C\u002Fp>\n\n\n\n\u003Cp>The \u003Ca href=\"http:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002Fabs\u002F10.1177\u002F2167702617691560?journalCode=cpxa\" target=\"_blank\" rel=\"noreferrer noopener\">machine-learning algorithm\u003C\u002Fa> created at Vanderbilt University Medical Center in Nashville, uses hospital admissions data, including age, gender, zip code, medication, and diagnostic history, to predict the likelihood of any given individual taking their own life. In trials using data \u003Ca href=\"https:\u002F\u002Fqz.com\u002F1367197\u002Fmachines-know-when-someones-about-to-attempt-suicide-how-should-we-use-that-information\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">gathered from more than 5,000 patients who had been admitted to the hospital for either self-harm or suicide attempts\u003C\u002Fa>, the algorithm was 84% accurate at predicting whether someone would attempt suicide the following week, and 80% accurate at predicting whether someone would attempt suicide within the following two years.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">8) AI predicting death risk among inpatients\u003C\u002Fh2>\n\n\n\n\u003Cp>Researchers at Stanford University trained an AI system to increase the number of inpatients who receive end-of-life care exactly when needed – meaning the smart algorithm is able to predict when very seriously ill patients are nearing the end of their lives.\u003C\u002Fp>\n\n\n\n\u003Cp>The \u003Ca href=\"https:\u002F\u002Fwww.fastcompany.com\u002F90157967\u002Fthis-ai-predicts-death-could-it-improve-end-of-life-care\" target=\"_blank\" rel=\"noreferrer noopener\">algorithm was trained to analyze diagnoses, prescriptions, demographics, and other factors within electronic health records during that 3 to 12 month period before a patient passed away\u003C\u002Fa>. Once trained, the algorithm was able to flag still-living patients in a hospital’s system that might be appropriate candidates for palliative care. When Stanford Hospital’s palliative care team assessed 50 randomly chosen patients that the algorithm had flagged as being at very high risk, the team found that all of them were appropriate to be referred. Beyond being able to accurately predict, the program also left decision-making in the hands of the doctor entirely. That could be a future model for algorithms and physicians working together as a team.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F11\u002Ftmf_article_339-01-768x432.png\" alt=\"robot android artificial intelligence AI algorithm human people man woman\" class=\"wp-image-47983\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F11\u002Ftmf_article_339-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F11\u002Ftmf_article_339-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F11\u002Ftmf_article_339-01-1536x864.png 1536w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">9) MedPaLM, the medical large language model\u003C\u002Fh2>\n\n\n\n\u003Cp>Large language models undoubtedly have changed this field forever, they are capable of such high-quality assistance that was never seen earlier. Only a few short weeks after the release of ChatGPT, Google\u002FDeepMind announced \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fpharmaphorum.com\u002Fnews\u002Fgoogle-and-deepmind-share-work-on-medical-chatbot-med-palm\u002F\" target=\"_blank\">the release of MedPaLM\u003C\u002Fa>, a large language model \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fmedpalm-new-ai-medical-chatbots-will-soon-be-better-than-waiting-for-a-doctor\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">specifically designed to answer healthcare-related questions\u003C\u002Fa>, based on their 540-billion parameter PaLM model.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"351\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F01\u002FMedpalm2-768x351.jpg\" alt=\"Medpalm google deepmind large languae model\" class=\"wp-image-48673\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F01\u002FMedpalm2-768x351.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F01\u002FMedpalm2.jpg 988w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>This model was trained on six existing medical Q&A datasets (NedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU), and the developer teams also created their own HealthSearchQA, using questions about medical conditions and the associated symptoms. At the moment MedPaLM can’t be tested by the general public, but you can read the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.13138.pdf\" target=\"_blank\">researcher’s paper here\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>Very recently \u003Ca href=\"https:\u002F\u002Fcloud.google.com\u002Fblog\u002Ftopics\u002Fhealthcare-life-sciences\u002Fsharing-google-med-palm-2-medical-large-language-model\" target=\"_blank\" rel=\"noreferrer noopener\">the latest iteration was also launched\u003C\u002Fa> and Google provided access to a select group of users, but we haven’t seen any studies related to the 2.0 version yet. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">10) The sepsis-watching algorithms\u003C\u002Fh2>\n\n\n\n\u003Cp>Duke University researchers developed a Sepsis Watch deep learning algorithm that 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. The university has been working on this algorithm for years and implemented the model in clinical work in 2018. \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.advisory.com\u002Fdaily-briefing\u002F2022\u002F04\u002F12\u002Fai-hospitals\" target=\"_blank\">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 rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.wsj.com\u002Farticles\u002Fhow-hospitals-are-using-ai-to-save-lives-11649610000\" target=\"_blank\">The Wall Street Journal reported\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F02\u002Ftmf_article_246-01-768x432.png\" alt=\"\" class=\"wp-image-32755\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F02\u002Ftmf_article_246-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F02\u002Ftmf_article_246-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F02\u002Ftmf_article_246-01-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F02\u002Ftmf_article_246-01.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">We better look at AI as our new colleague\u003C\u002Fh2>\n\n\n\n\u003Cp>There are many more excellent examples of smart algorithms in healthcare, and a lot more will come in the future. But the last one showed the essence of digital health: the best results are achieved by the cooperative work of artificial intelligence and human doctors. \u003C\u002Fp>\n\n\n\n\u003Cp>As artificial intelligence (AI) is set to revolutionise medical practice, it is vital for medical students, young and practising doctors to be well-prepared for the changing landscape. The rapid advancements in AI have brought about a major paradigm shift.\u003C\u002Fp>\n\n\n\n\u003Cp>I have already said many times before, AI will not replace doctors, but doctors using AI will replace those who are not keeping up with this revolution. \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.thinkific.com\u002Fcourses\u002Fintroduction-to-artificial-intelligence-in-medicine-and-healthcare\" target=\"_blank\">Embracing and integrating AI technologies into medical practice\u003C\u002Fa> will not only benefit patients but also enhance the careers of those who are well-equipped for this new era of medicine.\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",false,{"rendered":20,"protected":18},"\u003Cp>As artificial intelligence tools have been invading more or less every area of healthcare, we made a list to keep track of the top smart algorithms aiming for better diagnostics, more sophisticated patient care or further sighted predictions of diseases. \u003C\u002Fp>\n",6,22998,"closed",true,"","standard",{"_acf_changed":18,"footnotes":25},[29],504,[31,32,33,34,35,36,37,38,39,40,41,42],198,246,271,275,346,361,372,425,134,144,163,1228,[44],950,[],[47,48,49,50,51,52,53,54,55,56,57],1715,1723,1789,1805,1819,1833,2485,2689,2705,2739,2967,[59,12,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78],"post-22987","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-death","tag-future","tag-health","tag-healthcare","tag-pathology","tag-prediction","tag-radiology","tag-technology-2","tag-ai","tag-artificial-intelligence","tag-cancer-2","tag-artificial","project_category-medical-professionals",{"id":22,"alt_text":80,"caption":25,"description":25,"media_type":81,"media_details":82,"post":5,"source_url":110},"top AI algorithms","image",{"width":83,"height":84,"file":85,"sizes":86,"image_meta":108},1920,1080,"2019\u002F02\u002Fradiologist_001-scaled.png",{"medium":87,"large":93,"thumbnail":98,"medium_large":102,"1536x1536":103},{"file":88,"width":89,"height":90,"mime-type":91,"source_url":92},"radiologist_001-scaled-370x208.png","370","208","image\u002Fpng","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002Fradiologist_001-scaled-370x208.png",{"file":94,"width":95,"height":96,"mime-type":91,"source_url":97},"radiologist_001-scaled-768x432.png","768","432","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002Fradiologist_001-scaled-768x432.png",{"file":99,"width":100,"height":100,"mime-type":91,"source_url":101},"radiologist_001-scaled-150x150.png","150","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002Fradiologist_001-scaled-150x150.png",{"file":94,"width":95,"height":96,"mime-type":91,"source_url":97},{"file":104,"width":105,"height":106,"mime-type":91,"source_url":107},"radiologist_001-scaled-1536x864.png","1536","864","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002Fradiologist_001-scaled-1536x864.png",{"aperture":109,"credit":25,"camera":25,"caption":25,"created_timestamp":109,"copyright":25,"focal_length":109,"iso":109,"shutter_speed":109,"title":25,"orientation":109},"0","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F02\u002Fradiologist_001-scaled.png",{"related_posts":112,"related_posts_footer":116,"cta_type":25,"cta_color":25,"subtitle":25,"related_books":18,"key_takeaways":120},[113,114,115],13650,14848,10983,[117,118,119],10785,16745,16479,[121,123],{"title":122},"\u003Cp>As AI tools have been invading more or less every area of healthcare, we made a list to keep track of the top AI algorithms aiming for better diagnostics, more sophisticated patient care or further sighted predictions of diseases.\u003C\u002Fp>\n\u003Cp> \u003C\u002Fp>\n",{"title":124},"\u003Cp>By enumerating the top AI tools that we discovered in healthcare so far, we also aim to add what we believe is already useful for the work of medical professionals.\u003C\u002Fp>\n",{"yoast_wpseo_title":126,"yoast_wpseo_metadesc":127,"yoast_wpseo_canonical":13},"Top AI Algorithms In Healthcare - The Medical Futurist","The Medical Futurist made a list to keep track of the top AI algorithms aiming for better diagnostics or further sighted predictions in healthcare.",{"self":129,"collection":135,"about":138,"author":141,"replies":144,"version-history":147,"predecessor-version":151,"wp:featuredmedia":155,"wp:attachment":158,"wp:term":161,"curies":177},[130],{"href":131,"targetHints":132},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F22987",{"allow":133},[134],"GET",[136],{"href":137},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[139],{"href":140},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[142],{"embeddable":24,"href":143},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F6",[145],{"embeddable":24,"href":146},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=22987",[148],{"count":149,"href":150},17,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F22987\u002Frevisions",[152],{"id":153,"href":154},59573,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F22987\u002Frevisions\u002F59573",[156],{"embeddable":24,"href":157},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F22998",[159],{"href":160},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=22987",[162,165,168,171,174],{"taxonomy":163,"embeddable":24,"href":164},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=22987",{"taxonomy":166,"embeddable":24,"href":167},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=22987",{"taxonomy":169,"embeddable":24,"href":170},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=22987",{"taxonomy":172,"embeddable":24,"href":173},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=22987",{"taxonomy":175,"embeddable":24,"href":176},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=22987",[178],{"name":179,"href":180,"templated":24},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[182],{"id":119,"date":183,"date_gmt":184,"guid":185,"modified":187,"modified_gmt":188,"slug":189,"status":11,"type":12,"link":190,"title":191,"content":193,"excerpt":195,"author":21,"featured_media":197,"comment_status":23,"ping_status":23,"sticky":24,"template":25,"format":26,"meta":198,"categories":199,"tags":204,"project_category":212,"contact_email_category":213,"yst_prominent_words":214,"class_list":221,"better_featured_image":234,"acf":269,"yoast_meta":276,"_links":279},"2017-10-19T16:00:23","2017-10-19T14:00:23",{"rendered":186},"http:\u002F\u002Fmedicalfuturist.com\u002F?p=16479","2022-05-17T15:16:28","2022-05-17T13:16:28","no-precision-medicine-without-artificial-intelligence","https:\u002F\u002Fmedicalfuturist.com\u002Fno-precision-medicine-without-artificial-intelligence",{"rendered":192},"There Is No Precision Medicine Without Artificial Intelligence",{"rendered":194,"protected":18},"\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 2017. 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>\n\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\n\n\n\n\u003Cp>Artificial narrow intelligence (ANI) will most likely help healthcare move from traditional, „one-size-fits-all” medical solutions towards targeted treatments, personalized therapies, and uniquely composed drugs. In two words: precision medicine. However, before we let ANI take over the stage in healthcare, stakeholders should consider several ethical and legal issues. \u003C\u002Fp>\n\n\n\n\u003C!--more-->\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Moving away from generalized medicine to personalisation\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Cem>The article is based on a paper about \u003Ca href=\"http:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Ffull\u002F10.1080\u002F23808993.2017.1380516?scroll=top&needAccess=true\">\u003Cstrong>the role of A.I. in Precision Medicine\u003C\u002Fstrong>\u003C\u002Fa> that was published in Expert Review of Precision Medicine and Drug Development.\u003C\u002Fem>\u003C\u002Fp>\n\n\n\n\u003Cp>Classical medical practice puts large groups of people in their focus and tries to develop clinical solutions, drugs or treatment based on the needs of the statistical average person. Disruptive technologies change that perspective completely. The basis of that transformation is data. Physicians are able to collect a vast amount of medical information about the individual through cheap genome sequencing, big data analytics, health sensors, wearables or artificial intelligence. Based on that specific knowledge, medical professionals can \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fprecision-medicine-best-hope-fight-cancer\u002F\">move away from generalistic solutions towards personalization and precision\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cp>As disruptive technologies appear on the stage of healthcare, it becomes possible to get down even more deeply to the roots of diseases and treatments. The “one-size-fits-all” strategy will definitely start to crumble. It is the logical result of hundreds of years of medical research and accumulated knowledge. Currently, we know that everyone has a different genetic code, may react differently to pharmaceutics or may have a completely opposite reaction to treatment as assumed.\u003C\u002Fp>\n\n\n\n\u003Cp>So why should we treat everyone with the same drugs or with the same method? And one of the most efficient means for precision medicine is artificial intelligence.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-image\">\u003Cfigure class=\"aligncenter\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"518\" height=\"362\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Findividual-versus-population.jpg\" alt=\"precision medicine\" class=\"wp-image-16481\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Findividual-versus-population.jpg 518w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Findividual-versus-population-512x358.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Findividual-versus-population-358x250.jpg 358w\" sizes=\"auto, (max-width: 518px) 100vw, 518px\" \u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The place of artificial intelligence in precision medicine\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>As the \u003Ca href=\"https:\u002F\u002Fghr.nlm.nih.gov\u002Fprimer\u002Fprecisionmedicine\u002Fdefinition\">National Institutes of Health (NIH) put\u003C\u002Fa> it, precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” To be able to ponder all those individual variations, medical professionals have to gather incredible amounts of information, and the ability to analyze, store, normalize or trace that data.\u003C\u002Fp>\n\n\n\n\u003Cp>Big data analytics is one area where A.I., especially ANI comes into the picture. Within a couple of years, it will most probably analyze big medical data sets, draw conclusions, find new correlations based on existing precedences and support the doctor’s job e.g. in decision-making. Several companies recognized already the immense potential in A.I. for mining medical records (Google Deepmind and \u003Ca href=\"https:\u002F\u002Fwww.ibm.com\u002Fwatson\u002Fhealth\u002Foncology\u002F\">IBM Watson\u003C\u002Fa>), identifying therapies (Zephyr Health), supporting radiology (\u003Ca href=\"http:\u002F\u002Fwww.enlitic.com\u002F\">Enlitic\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Farterys.com\u002F\">Arterys\u003C\u002Fa>, \u003Ca href=\"http:\u002F\u002Fwww.3scan.com\u002F\">3Scan\u003C\u002Fa>) or genomics (\u003Ca href=\"https:\u002F\u002Fwww.deepgenomics.com\u002F\">Deep Genomics\u003C\u002Fa>). My personal favorite is \u003Ca href=\"http:\u002F\u002Fwww.atomwise.com\u002F\">Atomwise\u003C\u002Fa>, which uses supercomputers that root out therapies from a database of molecular structures. In 2015, Atomwise launched a virtual search for safe, existing medicines that could be redesigned to treat the Ebola virus. They found two drugs predicted by the company’s A.I. technology which may significantly 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\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Medical limitations and ethical issues around A.I.\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>To avoid over-hyping technology, the medical limitations of present-day A.I. have to be acknowledged. In the case of image recognition and using machine learning and deep learning algorithms for the purposes of radiology, there is the risk of feeding the computer not only with thousands of images but underlying bias. For example, the images tend to originate from one part of the U.S or the framework for conceptualizing the algorithm itself incorporates the subjective assumptions of the working team. Moreover, the forecasting and predictive abilities of smart algorithms are anchored in precedences – however, they might be useless in novel cases of drug side effects or treatment resistance.\u003C\u002Fp>\n\n\n\n\u003Cp>Yet, medical as well as technological limitations of A.I. as well as ANI will still be easier to overcome than ethical and legal issues. Who is to blame if a smart algorithm makes a mistake and does not spot a cancerous nodule on a lung X-ray? To whom to turn to when A.I. comes up with a false prediction? Who will build in safety features? What will be the rules and regulations to decide on safety?\u003C\u002Fp>\n\n\n\n\u003Cp>Although these burning questions cannot be answered in their entirety today, we have to do some preparations to be able to keep the human touch at the center of medicine and avert the possibility of A.I. becoming an existential threat to mankind feared by \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002F2017\u002Fsep\u002F04\u002Felon-musk-ai-third-world-war-vladimir-putin\">Elon Musk\u003C\u002Fa> or \u003Ca href=\"http:\u002F\u002Fwww.bbc.com\u002Fnews\u002Ftechnology-30290540\">Stephen Hawking\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"870\" height=\"489\" src=\"https:\u002F\u002F1712507217.rsc.cdn77.org\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fmind-and-machine.jpg\" alt=\"precision medicine\" class=\"wp-image-16483\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fmind-and-machine.jpg 870w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fmind-and-machine-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fmind-and-machine-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fmind-and-machine-512x288.jpg 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fmind-and-machine-444x250.jpg 444w\" sizes=\"auto, (max-width: 870px) 100vw, 870px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>What should stakeholders do to avoid the A.I. apocalypse?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cul class=\"wp-block-list\">\u003Cli>\u003Cstrong>Set up ethical standards\u003C\u002Fstrong> how to use A.I. on the micro and macro levels of the healthcare sector. We need specific guidelines starting from the smallest units (medical professionals) to the most complex ones (national-level healthcare systems). \u003Cstrong>The principle of human comes first\u003C\u002Fstrong> should stand at the core of these standards.\u003C\u002Fli>\u003Cli>\u003Cstrong>A. I. should be implemented cautiously and gradually\u003C\u002Fstrong> in order to give time and space for mapping the potential risks and downsides. Independent bioethical research groups, as well as medical watchdogs, should monitor the process closely.\u003C\u002Fli>\u003Cli>\u003Cstrong>Medical professionals \u003C\u002Fstrong>should familiarize with the basic concepts and working methods of A.I. in a medical setting to get over their potential fears and understand how the technology could help their work. There are concerns that A.I. will take over plenty of jobs in healthcare, yet, I believe the key is cooperation. Medical professionals should work together with technology if they want to achieve their full potential to heal patients.\u003C\u002Fli>\u003Cli>\u003Cstrong>Patients \u003C\u002Fstrong>should also explore A.I. in detail and how it might change their own everyday lives. It is important as in a couple of years, \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fkid-will-play-friends-learn-vr-teachers\u002F\">kids will probably play with A.I. friends\u003C\u002Fa> such as the cute, dinosaur-shaped Cognitoys or learn from virtual reality teachers.\u003C\u002Fli>\u003Cli>\u003Cstrong>Companies that develop A.I. solutions\u003C\u002Fstrong> should communicate clearly and concisely towards the general public about the potential risks of utilizing A.I. in medicine. That’s also useful to avoid overhyping technology.\u003C\u002Fli>\u003Cli>\u003Cstrong>Decision-makers at healthcare institutions & policy-makers \u003C\u002Fstrong>should guide the process of implementing A.I. in healthcare along the principles and ethical standards they work out with other industry stakeholders. Moreover, they should push companies towards putting affordable A.I. solutions on the table and keeping the focus on the patient all the time.\u003C\u002Fli>\u003C\u002Ful>\n\n\n\n\u003Cp>I have no doubts that \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fibm-watson-is-the-stethoscope-of-the-21st-century\u002F\">A.I. will be the stethoscope of the 21st century\u003C\u002Fa> and the backbone of precision medicine. It has the biggest potential to analyze vast amounts of data and offer insights to create personalized solutions and targeted treatments. Yet, we have to do everything in our power to ensure that A.I. remains safe, secure and efficient in fulfilling its mission as an aid in healing patients and helping the medical scene.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Ca href=\"https:\u002F\u002Fthemedicalfuturist.us8.list-manage.com\u002Fsubscribe?u=5b42ebb547c75ff669a6572d3&id=efd6a3cd08\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\u002F\u002Fwww.google.com\u002Furl?q=https:\u002F\u002Fthemedicalfuturist.us8.list-manage.com\u002Fsubscribe?u%3D5b42ebb547c75ff669a6572d3%26id%3Defd6a3cd08&source=gmail&ust=1534355444917000&usg=AFQjCNH9L0BP3FQtAahXcvw9Dg9JLtyZDw\">\u003Cb>Subscribe To The Medical Futurist℠ Newsletter\u003C\u002Fb>\u003C\u002Fa>\u003C\u002Fh2>\n\n\n\n\u003Cul class=\"wp-block-list\">\u003Cli>News shaping the future of healthcare\u003C\u002Fli>\u003Cli>Advice on taking charge of your health\u003C\u002Fli>\u003Cli>Reviews of the latest health technology\u003C\u002Fli>\u003C\u002Ful>\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":196,"protected":18},"\u003Cp>Artificial narrow intelligence (ANI) will most likely help healthcare move from traditional, „one-size-fits-all” medical solutions towards targeted treatments, personalized therapies, and uniquely composed drugs. In two words: precision medicine. However, before we let ANI take over the stage in healthcare, stakeholders should consider several ethical and legal issues.\u003C\u002Fp>\n",16482,{"_acf_changed":18,"footnotes":25},[29,200,201,202,203],511,521,499,489,[34,205,206,207,208,209,39,210,40,32,211],671,289,749,304,313,519,608,[],[],[215,216,47,48,217,218,219,220],1661,1683,1729,1739,1831,1883,[222,12,60,61,62,63,64,65,223,224,225,226,69,227,228,229,230,231,74,232,75,67,233],"post-16479","category-bioethics","category-future-medicine","category-healthcare-design","category-personalized-medicine","tag-machine-learning","tag-innovation","tag-ethical","tag-medical","tag-medicine","tag-gc4","tag-precision-medicine",{"id":197,"alt_text":235,"caption":25,"description":25,"media_type":81,"media_details":236,"post":119,"source_url":268},"precision medicine",{"width":237,"height":203,"file":238,"sizes":239,"image_meta":266},870,"2017\u002F10\u002Fai-in-healthcare.jpg",{"medium":240,"large":246,"thumbnail":251,"medium_large":255,"large_old_512x288":256,"medium_old_444x250":261},{"file":241,"width":242,"height":243,"mime-type":244,"source_url":245},"ai-in-healthcare-370x208.jpg",370,208,"image\u002Fjpeg","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fai-in-healthcare-370x208.jpg",{"file":247,"width":248,"height":249,"mime-type":244,"source_url":250},"ai-in-healthcare-768x432.jpg",768,432,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fai-in-healthcare-768x432.jpg",{"file":252,"width":253,"height":253,"mime-type":244,"source_url":254},"ai-in-healthcare-150x150.jpg",150,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fai-in-healthcare-150x150.jpg",{"file":247,"width":248,"height":249,"mime-type":244,"source_url":250},{"file":257,"width":258,"height":259,"mime-type":244,"source_url":260},"ai-in-healthcare-512x288.jpg",512,288,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fai-in-healthcare-512x288.jpg",{"file":262,"width":263,"height":264,"mime-type":244,"source_url":265},"ai-in-healthcare-444x250.jpg",444,250,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fai-in-healthcare-444x250.jpg",{"aperture":109,"credit":25,"camera":25,"caption":25,"created_timestamp":109,"copyright":25,"focal_length":109,"iso":109,"shutter_speed":109,"title":25,"orientation":109,"keywords":267},[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2017\u002F10\u002Fai-in-healthcare.jpg",{"related_posts":270,"related_posts_footer":274,"cta_type":25,"cta_color":25,"subtitle":25,"related_books":18},[271,272,273],15351,15713,21320,[275,118,117],17898,{"yoast_wpseo_title":277,"yoast_wpseo_metadesc":278,"yoast_wpseo_canonical":190},"There Is No Precision Medicine Without Artificial Intelligence - The Medical Futurist","A.I. will be the backbone of precision medicine. It has the biggest potential to analyze vast amounts of data and offer insights to create personalized solutions and targeted treatments.",{"self":280,"collection":285,"about":287,"author":289,"replies":291,"version-history":294,"predecessor-version":298,"wp:featuredmedia":302,"wp:attachment":305,"wp:term":308,"curies":319},[281],{"href":282,"targetHints":283},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F16479",{"allow":284},[134],[286],{"href":137},[288],{"href":140},[290],{"embeddable":24,"href":143},[292],{"embeddable":24,"href":293},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=16479",[295],{"count":296,"href":297},23,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F16479\u002Frevisions",[299],{"id":300,"href":301},45599,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F16479\u002Frevisions\u002F45599",[303],{"embeddable":24,"href":304},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F16482",[306],{"href":307},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=16479",[309,311,313,315,317],{"taxonomy":163,"embeddable":24,"href":310},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=16479",{"taxonomy":166,"embeddable":24,"href":312},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=16479",{"taxonomy":169,"embeddable":24,"href":314},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=16479",{"taxonomy":172,"embeddable":24,"href":316},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=16479",{"taxonomy":175,"embeddable":24,"href":318},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=16479",[320],{"name":179,"href":180,"templated":24},[],1789237883182]