[{"data":1,"prerenderedAt":159},["ShallowReactive",2],{"slug-what-ai-can-do-in-healthcare-in-the-coming-years-8-examples":3},{"post":4,"relatedPosts":157,"relatedBooks":158},{"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":34,"project_category":35,"contact_email_category":36,"yst_prominent_words":37,"class_list":48,"better_featured_image":58,"acf":96,"yoast_meta":102,"_links":104},58279,"2024-12-11T08:30:16","2024-12-11T07:30:16",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=58279&_wpnonce=0f33c73ed6&status=auto-draft&type=post","2024-12-11T08:30:17","2024-12-11T07:30:17","what-ai-can-do-in-healthcare-in-the-coming-years-8-examples","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fwhat-ai-can-do-in-healthcare-in-the-coming-years-8-examples",{"rendered":17},"What AI Can Do In Healthcare In The Coming Years: 8 Examples",{"rendered":19,"protected":20},"\n\u003Cp>We previously looked at what artificial intelligence (AI) can do already in healthcare and we continue this series on the technology’s potential in this article. This time, our focus is on what we can expect the technology to do in the near future. We share 8 examples of what AI is likely to do in the healthcare field in order to better anticipate the future.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>1. \u003C\u002Fstrong>\u003Cstrong>Predicting disease progression\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Prior to the worsening of a condition, there are often telltale signals from various health metrics that indicate a downward trend. However, such signals are not easily picked up, which leads to \u003Ca href=\"https:\u002F\u002Fdeepmind.google\u002Fdiscover\u002Fblog\u002Fusing-ai-to-give-doctors-a-48-hour-head-start-on-life-threatening-illness\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">millions of deaths\u003C\u002Fa> that could be prevented with earlier detection.\u003C\u002Fp>\n\n\n\n\u003Cp>AI models have been developed to analyse electronic health records (EHRs) to accurately predict long-term outcomes for various conditions. Google DeepMind has developed an algorithm that can \u003Ca href=\"https:\u002F\u002Fdeepmind.google\u002Fdiscover\u002Fblog\u002Fusing-ai-to-give-doctors-a-48-hour-head-start-on-life-threatening-illness\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">accurately predict acute kidney injury\u003C\u002Fa> in patients up to 48 hours earlier than it is currently diagnosed. Researchers in Belgium have trained an AI model to reliably predict \u003Ca href=\"https:\u002F\u002Fhealthcare-in-europe.com\u002Fen\u002Fnews\u002Fmultiple-sclerosis-prediction-progression-ai.html\" target=\"_blank\" rel=\"noreferrer noopener\">the probability of disability progression\u003C\u002Fa> in the next two years among people with multiple sclerosis.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_406-768x432.png\" alt=\"AI, doctor, screen, diagnosis\" class=\"wp-image-55315\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_406-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_406-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_406-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_406-2048x1152.png 2048w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>While such models are in the research and testing stages, we can expect them to be implemented in practice in the coming years \u003Ca href=\"https:\u002F\u002Fwww.cam.ac.uk\u002Fresearch\u002Fnews\u002Fartificial-intelligence-outperforms-clinical-tests-at-predicting-progress-of-alzheimers-disease\" target=\"_blank\" rel=\"noreferrer noopener\">as they scale up\u003C\u002Fa>. However, this is subject to clinicians \u003Ca href=\"https:\u002F\u002Fdeepmind.google\u002Fdiscover\u002Fblog\u002Fusing-ai-to-give-doctors-a-48-hour-head-start-on-life-threatening-illness\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">having the adequate tools\u003C\u002Fa> to be alerted of disease progression and acting at the right time.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>2. \u003C\u002Fstrong>\u003Cstrong>Real-time, real surgical assistance\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Unequal distribution of specialist surgical workforce disproportionately affects rural and resource-poor areas. Some models even estimate that such regions experience \u003Ca href=\"https:\u002F\u002Fwww.facs.org\u002Ffor-medical-professionals\u002Fnews-publications\u002Fnews-and-articles\u002Fbulletin\u002F2024\u002Fjune-2024-volume-109-issue-6\u002Fai-has-potential-to-transform-global-surgical-systems\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">a shortage of one million\u003C\u002Fa> specialist surgical, anesthetic, and obstetric providers. AI could bridge that gap by enabling remote collaboration in surgical rooms.\u003C\u002Fp>\n\n\n\n\u003Cp>AI-powered systems like \u003Ca href=\"https:\u002F\u002Fwww.proximie.com\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Proximie\u003C\u002Fa> connect surgeons virtually to any operating room in real-time. This enables collaboration and broadens surgical expertise access while the AI provides performance metrics and improves workflow. Similar solutions are also on the way. For example, in early 2024 \u003Ca href=\"https:\u002F\u002Fwww.fiercebiotech.com\u002Fmedtech\u002Fcutting-meet-edge-jj-medtech-taps-nvidia-surgery-ai-partnership\" target=\"_blank\" rel=\"noreferrer noopener\">Johnson & Johnson MedTech partnered with Nvidia\u003C\u002Fa> to develop AI tools aimed at delivering real-time analyses of surgical data.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>3. Coping with alarm fatigue\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>In healthcare, \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-could-solve-alarm-fatigue-in-hospitals\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">alarm fatigue\u003C\u002Fa> refers to a phenomenon where caregivers become desensitized to alarm signs from numerous beeping devices. In fact, \u003Ca href=\"https:\u002F\u002Fnurse.org\u002Farticles\u002Falarm-fatigue-statistics-patient-safety\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">between 72% and 99%\u003C\u002Fa> of all alarms are false which adds to alarm fatigue. Unfortunately, this means that some alarms that actually necessitate clinical attention are overlooked, leading to medical mistakes. \u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-768x432.png\" alt=\"alarm fatigue\" class=\"wp-image-26271\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small-512x288.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2020\u002F02\u002Falarm-fatigue-small.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>AI could \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fartificial-intelligence-could-solve-alarm-fatigue-in-hospitals\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">tune out the false alarms\u003C\u002Fa> and make clinical staff aware of the alarms that require their attention. Researchers \u003Ca href=\"https:\u002F\u002Fwww.jmir.org\u002F2019\u002F11\u002Fe15406\" target=\"_blank\" rel=\"noreferrer noopener\">have developed an algorithm\u003C\u002Fa> to reduce notifications received by the caregivers by up to 99.3%. This automated AI reasoning mechanism analyses patient monitoring data and vital signs to decide whether to group notifications rather than send individual ones so as to prevent alarm fatigue. \u003C\u002Fp>\n\n\n\n\u003Cp>Medtronic has partnered with other institutions \u003Ca href=\"https:\u002F\u002Fthedailyscan.providencehealthcare.org\u002F2024\u002F09\u002Fusing-ai-to-address-alarm-fatigue\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">to develop a similar tool\u003C\u002Fa>. Their ‘Beyond the Noise’ project aims to develop an AI filtration tool to ensure only critical alerts reach medical staff. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>4. Remote patient monitoring\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Remote care has been on the rise in recent years and such modalities can be \u003Ca href=\"https:\u002F\u002Fwww.jmir.org\u002F2024\u002F1\u002Fe53266\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">enhanced with wearables\u003C\u002Fa> for at-home monitoring. We have become accustomed with the likes of \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fapple-watch-series-9-review-from-health-monitoring-to-lifestyle-integration\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">smartwatches\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffinally-a-good-wearable-blood-pressure-monitor-aktiia-bracelet-review\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">blood pressure monitors\u003C\u002Fa>; but the next wave of wearables will increasingly be combined with AI.\u003C\u002Fp>\n\n\n\n\u003Cp>One example is that of \u003Ca href=\"https:\u002F\u002Fwww.biofourmis.com\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Biofourmis\u003C\u002Fa> which provides care-at-home solutions for the continuous monitoring of both acute and chronic patients. Their platform combines clinical-grade wearable devices and AI algorithms for remote monitoring and detection of deterioration. The company has noted a \u003Ca href=\"https:\u002F\u002Fwww.biofourmis.com\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">49% decrease\u003C\u002Fa> in readmission rates for patients with congestive heart failure and the ability to detect deterioration 21 hours sooner.\u003C\u002Fp>\n\n\n\n\u003Cp>The makers of the fitness tracker \u003Ca href=\"https:\u002F\u002Fwww.whoop.com\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Whoop\u003C\u002Fa> are also considering the integration of AI. Will Ahmed, the company’s founder, \u003Ca href=\"https:\u002F\u002Fwww.menshealth.com\u002Fuk\u002Fhealth\u002Fa63081240\u002Ffuture-ai-whoop\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">highlights AI’s unique ability\u003C\u002Fa> to notice health trends or crunch biometric numbers for personalised health insights.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>5. Genomics and precision medicine\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>One’s genetic makeup can be responsible for \u003Ca href=\"https:\u002F\u002Fhealth.google\u002Fhealth-research\u002Fgenomics\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">30% of individual health outcomes\u003C\u002Fa>. As such, genetic and genomic analyses can provide \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fi-got-my-whole-genome-sequenced-heres-what-i-learned\" target=\"_blank\" rel=\"noreferrer noopener\">valuable insights\u003C\u002Fa> into the risk of developing certain conditions and help in mitigating them. Such tests have become more affordable over the years but the ability to draw meaningful interpretations \u003Ca href=\"https:\u002F\u002Fhealth.google\u002Fhealth-research\u002Fgenomics\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">remains a barrier\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F08\u002Ftmf_article_426-01-768x432.png\" alt=\"gene therapy\" class=\"wp-image-57337\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F08\u002Ftmf_article_426-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F08\u002Ftmf_article_426-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F08\u002Ftmf_article_426-01-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F08\u002Ftmf_article_426-01.png 1820w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>This hurdle can be addressed with AI tools like \u003Ca href=\"https:\u002F\u002Fresearch.google\u002Fblog\u002Flearning-deepvariants-hidden-powers\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Google’s DeepVariant\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.helix.com\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Helix\u003C\u002Fa>. These models can analyse genetic data and improve the identification of disease-causing variants. Subsequently, such findings can help in the development of more personalised treatments. DeepVariant can \u003Ca href=\"https:\u002F\u002Fresearch.google\u002Fblog\u002Flearning-deepvariants-hidden-powers\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">reduce the error rate\u003C\u002Fa> of identifying variant locations by more than 50%. Helix’s AI \u003Ca href=\"https:\u002F\u002Fwww.drugdiscoverytrends.com\u002Fhelix-recursion-ai-driven-genomic-drug-discovery\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">can mine genomic data\u003C\u002Fa> for more targeted drug discovery.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>6. Automated insurance approvals and billing\u003C\u002Fstrong> \u003C\u002Fh2>\n\n\n\n\u003Cp>The manual handling of health insurance claims has been a reason for significant turndowns. In some cases, insurers \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fshashankagarwal\u002F2024\u002F03\u002F28\u002Fthe-ai-revolution-in-medical-claims-processing\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">have denied 49% of claims\u003C\u002Fa>. AI systems can make this process \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fshashankagarwal\u002F2024\u002F03\u002F28\u002Fthe-ai-revolution-in-medical-claims-processing\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">more efficient\u003C\u002Fa>. They can streamline prior authorizations and claim processes, reducing administrative delays. The patient experience is also made smoother as wait times are reduced.\u003C\u002Fp>\n\n\n\n\u003Cp>Despite such promises, some companies are facing financial and ethical challenges. Healthcare AI startup Olive, which focused on revenue cycle automation tools, \u003Ca href=\"https:\u002F\u002Fwww.healthcaredive.com\u002Fnews\u002Folive-ai-shuts-down\u002F698455\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">closed down\u003C\u002Fa> due to strained resources after a period of fast growth. Other tools like NaviHealth \u003Ca href=\"https:\u002F\u002Fwww.newsweek.com\u002Fhospitals-are-reporting-more-insurance-denials-ai-driving-them-1977706\" target=\"_blank\" rel=\"noreferrer noopener\">appear to have targeted\u003C\u002Fa> beneficiaries of Medicare Advantage to deny them care. This highlights the need for adequate moderation of such tools to ensure ethical and equitable automation.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>7.\u003C\u002Fstrong> \u003Cstrong>Early detection of rare diseases\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>By virtue of their uncommon occurrence, rare diseases are challenging to identify and treat. They can sometimes present with specific physical features that can help in their identification. Clinicians can expect to receive the aid of AI to help them detect such conditions. \u003C\u002Fp>\n\n\n\n\u003Cp>Researchers in Germany \u003Ca href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41436-019-0566-2\" target=\"_blank\" rel=\"noreferrer noopener\">developed such a tool\u003C\u002Fa>, trained on multitude photographs, to\u003Cem> \u003C\u002Fem>improve the accuracy of detecting rare conditions such as mucopolysaccharidosis, Mabry syndrome and Kabuki syndrome, where those affected have characteristic facial features. \u003C\u002Fp>\n\n\n\n\u003Cp>Another similar tool comes from \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.om1.com\u002F\" target=\"_blank\">OM1\u003C\u002Fa>. Its AI analyses patterns from patient data to \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.dermatologytimes.com\u002Fview\u002Frare-disease-detection-with-ai-what-tools-to-trust\" target=\"_blank\">form a “digital phenotype”\u003C\u002Fa> that can be used to detect early warning signals of rare conditions such as generalized pustular psoriasis. \u003C\u002Fp>\n\n\n\n\u003Cp>Using such techniques could fast-track the identification and treatment of those affected from early on based on patient-reported symptoms and clinical data.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>8. Patient-specific virtual health coaches\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Health coaching \u003Ca href=\"https:\u002F\u002Fwww.england.nhs.uk\u002Flong-read\u002Fhealth-coaching\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">can train people\u003C\u002Fa> to adopt healthier behaviours and reduce the risk of preventable diseases. This approach has enticed companies to integrate AI for patient-specific virtual health coaching. \u003C\u002Fp>\n\n\n\n\u003Cp>One major player is OpenAI which \u003Ca href=\"https:\u002F\u002Ftechcrunch.com\u002F2024\u002F11\u002F14\u002Fsam-altman-and-arianna-huffingtons-thrive-ai-health-assistant-has-a-bare-bones-demo\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">has partnered with\u003C\u002Fa> Thrive AI Health to develop an AI-powered health coach. Other apps like \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fhealthify\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Healthify\u003C\u002Fa> track diet intake and provide personalised health coaching with the assistance of AI. We can expect such types of virtual health coaches to become more commonplace in the near future.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"393\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F12\u002Fai-virtual-health-coach-768x393.png\" alt=\"\" class=\"wp-image-58283\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F12\u002Fai-virtual-health-coach-768x393.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F12\u002Fai-virtual-health-coach-1536x785.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F12\u002Fai-virtual-health-coach.png 1653w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: https:\u002F\u002Fopenai.com\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>This concludes our collection of what AI is likely to do in healthcare. We would encourage you to also take a look at our first entry in this series of articles to learn more about what AI can already do in this field. We will be back with a final entry focusing on what the technology might bring to healthcare.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cem>Written by Dr. Bertalan Meskó & Dr. Pranavsingh Dhunnoo\u003C\u002Fem>\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>We share 8 examples of what AI is likely to do in the healthcare field in order to better anticipate the future.\u003C\u002Fp>\n",16,54203,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33],7079,504,521,[],[],[],[38,39,40,41,42,43,44,45,46,47],1683,1693,1833,2369,2389,2719,4675,1571,1639,1675,[49,14,50,51,52,53,54,55,56,57],"post-58279","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tmf","category-artificial-intelligence","category-future-medicine",{"id":24,"alt_text":27,"caption":27,"description":27,"media_type":59,"media_details":60,"post":94,"source_url":95},"image",{"width":61,"height":62,"file":63,"filesize":64,"sizes":65,"image_meta":91},1920,1080,"2023\u002F12\u002Ftmf_article_362_AI_doctor_robot.png",638945,{"medium":66,"large":73,"thumbnail":79,"medium_large":84,"1536x1536":85},{"file":67,"width":68,"height":69,"mime-type":70,"filesize":71,"source_url":72},"tmf_article_362_AI_doctor_robot-370x208.png",370,208,"image\u002Fpng",50581,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F12\u002Ftmf_article_362_AI_doctor_robot-370x208.png",{"file":74,"width":75,"height":76,"mime-type":70,"filesize":77,"source_url":78},"tmf_article_362_AI_doctor_robot-768x432.png",768,432,143387,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F12\u002Ftmf_article_362_AI_doctor_robot-768x432.png",{"file":80,"width":81,"height":81,"mime-type":70,"filesize":82,"source_url":83},"tmf_article_362_AI_doctor_robot-150x150.png",150,19651,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F12\u002Ftmf_article_362_AI_doctor_robot-150x150.png",{"file":74,"width":75,"height":76,"mime-type":70,"filesize":77,"source_url":78},{"file":86,"width":87,"height":88,"mime-type":70,"filesize":89,"source_url":90},"tmf_article_362_AI_doctor_robot-1536x864.png",1536,864,381270,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F12\u002Ftmf_article_362_AI_doctor_robot-1536x864.png",{"aperture":92,"credit":27,"camera":27,"caption":27,"created_timestamp":92,"copyright":27,"focal_length":92,"iso":92,"shutter_speed":92,"title":27,"orientation":92,"keywords":93},"0",[],54197,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F12\u002Ftmf_article_362_AI_doctor_robot.png",{"subtitle":27,"key_takeaways":97,"cta_type":27,"cta_color":27,"related_books":20,"related_posts_footer":20,"related_posts":20},[98,100],{"title":99},"\u003Cp>We continue our series on the potentials of artificial intelligence (AI) in healthcare, this time focusing on what we can expect the technology to do in the near future.\u003C\u002Fp>\n",{"title":101},"\u003Cp>This entry shares a collection of 8 examples of what AI is likely to do in the healthcare field which range from real-time surgical assistance to virtual health coaches.\u003C\u002Fp>\n",{"yoast_wpseo_title":103,"yoast_wpseo_metadesc":27,"yoast_wpseo_canonical":15},"What AI Can Do In Healthcare In The Coming Years: 8 Examples - The Medical Futurist",{"self":105,"collection":111,"about":114,"author":117,"replies":120,"version-history":123,"predecessor-version":127,"wp:featuredmedia":131,"wp:attachment":134,"wp:term":137,"curies":153},[106],{"href":107,"targetHints":108},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F58279",{"allow":109},[110],"GET",[112],{"href":113},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[115],{"href":116},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[118],{"embeddable":26,"href":119},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[121],{"embeddable":26,"href":122},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=58279",[124],{"count":125,"href":126},4,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F58279\u002Frevisions",[128],{"id":129,"href":130},58297,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F58279\u002Frevisions\u002F58297",[132],{"embeddable":26,"href":133},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F54203",[135],{"href":136},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=58279",[138,141,144,147,150],{"taxonomy":139,"embeddable":26,"href":140},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=58279",{"taxonomy":142,"embeddable":26,"href":143},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=58279",{"taxonomy":145,"embeddable":26,"href":146},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=58279",{"taxonomy":148,"embeddable":26,"href":149},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=58279",{"taxonomy":151,"embeddable":26,"href":152},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=58279",[154],{"name":155,"href":156,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[],[],1788949133367]