{"id":19657,"date":"2025-04-21T05:06:10","date_gmt":"2025-04-21T05:06:10","guid":{"rendered":"https:\/\/engineerbabu.com\/blog\/?p=19657"},"modified":"2025-12-15T07:23:47","modified_gmt":"2025-12-15T07:23:47","slug":"9-ai-use-cases-in-healthcare-app-development","status":"publish","type":"post","link":"https:\/\/engineerbabu.com\/blog\/9-ai-use-cases-in-healthcare-app-development\/","title":{"rendered":"9 AI Use cases in Healthcare App Development"},"content":{"rendered":"\r\n<p>Here\u2019s something most founders don\u2019t like hearing: your healthcare app isn\u2019t slow because of dev speed, and it\u2019s not underperforming because of budget. It\u2019s probably just not smart enough yet.<\/p>\r\n\r\n\r\n\r\n<p>That\u2019s what the numbers are telling us.<\/p>\r\n\r\n\r\n\r\n<p>Healthcare apps that use AI are pulling ahead\u2014fast. They\u2019re reducing diagnostic errors by 30%, slashing patient no-show rates with AI nudges, and flagging high-risk patients before nurses even walk into the room. And get this\u2014AI-powered apps are expected to power <strong>$102 billion<\/strong> worth of healthcare workflows by 2027.<\/p>\r\n\r\n\r\n\r\n<p>This isn\u2019t about adding a chatbot and calling it \u201cintelligent.\u201d It\u2019s about giving your app the ability to think, learn, and act\u2014without a human in the loop 24\/7.<\/p>\r\n\r\n\r\n\r\n<p>In this post, we\u2019re breaking down <strong>9 real AI use cases<\/strong> that are already working in the field\u2014from diagnostics to drug discovery to workflow automation. These aren\u2019t theoretical. These are battle-tested use cases you could build or integrate <em>right now<\/em>.<\/p>\r\n\r\n\r\n\r\n<h2 id=\"h-9-real-life-ai-use-cases-in-healthcare-app-development\" class=\"wp-block-heading\"><strong>9 Real-Life AI Use Cases in Healthcare App Development<\/strong><\/h2>\r\n\r\n\r\n\r\n<h3 id=\"h-ai-powered-diagnostics\" class=\"wp-block-heading\"><strong>AI-Powered Diagnostics<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>AI can now spot diseases faster\u2014and sometimes more accurately\u2014than human doctors.<\/p>\r\n\r\n\r\n\r\n<p>That\u2019s not just a headline. It\u2019s real, and it\u2019s happening in production apps. AI is being used to detect cancer, heart disease, diabetic retinopathy, and more\u2014often in seconds. A study showed that AI models matched radiologists in interpreting breast cancer screenings with <a href=\"https:\/\/ejrnm.springeropen.com\/articles\/10.1186\/s43055-023-01136-4#:~:text=Artificial%20intelligence%20was%20found%20to,100%25)%20than%20AI%20(93.7\" target=\"_blank\" rel=\"noopener\">up to 94.5% accuracy<\/a>.<\/p>\r\n\r\n\r\n\r\n<p>One of the most talked-about tools? <strong>IBM Watson for Oncology<\/strong>. It analyzes clinical data and medical literature to help recommend cancer treatments. There\u2019s also <strong>Aidoc<\/strong>, an AI tool used by hospitals to flag strokes and internal bleeds in CT scans in under five minutes.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Cuts down diagnosis time<\/li>\r\n\r\n\r\n\r\n<li>Reduces human error<\/li>\r\n\r\n\r\n\r\n<li>Scales expert-level insights across clinics and regions<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h3 id=\"h-virtual-health-assistants\" class=\"wp-block-heading\"><strong>Virtual Health Assistants<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Your users don\u2019t want to call a clinic. They want answers now\u2014and AI can give them that.<\/p>\r\n\r\n\r\n\r\n<p>Virtual health assistants, powered by AI, are reshaping how patients interact with healthcare apps. These assistants can answer questions, assess symptoms, schedule appointments, and even remind patients to take their meds.<\/p>\r\n\r\n\r\n\r\n<p>One example? <strong>Ada Health<\/strong>. Their AI-powered symptom checker has over <strong>12 million users<\/strong> and can suggest possible conditions after asking the user a series of medical questions. It\u2019s like a triage nurse, but one that never sleeps.<\/p>\r\n\r\n\r\n\r\n<p>There\u2019s also <strong>Mayo Clinic\u2019s AI chatbot<\/strong>, which saw a surge in usage during the COVID-19 pandemic. It handled more than <strong>300,000 interactions per day<\/strong>, helping users navigate symptoms and get care advice without overwhelming call centers.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Cuts down patient wait times<\/li>\r\n\r\n\r\n\r\n<li>Frees up staff from routine queries<\/li>\r\n\r\n\r\n\r\n<li>Makes apps feel more responsive and personalized<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<p>It\u2019s not just chatbots either. Voice assistants that transcribe patient interactions or route them to the right department? That\u2019s AI\u2014and it\u2019s becoming the new normal.<\/p>\r\n\r\n\r\n\r\n<h3 id=\"h-remote-patient-monitoring-rpm\" class=\"wp-block-heading\"><strong>Remote Patient Monitoring (RPM)<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>AI is helping doctors keep an eye on patients\u2014even when they\u2019re not in the hospital.<\/p>\r\n\r\n\r\n\r\n<p>Remote Patient Monitoring combines AI with wearables and mobile apps to track vitals like heart rate, blood pressure, blood glucose, oxygen levels, and more. The magic happens when AI jumps in to detect patterns or catch early warning signs that humans might miss.<\/p>\r\n\r\n\r\n\r\n<p>Take <strong>Biofourmis<\/strong>, a healthtech unicorn using AI to monitor patients with chronic conditions. Their platform analyzes continuous data streams and can predict hospital readmissions <strong>up to 7 days in advance<\/strong>. That\u2019s a game-changer for managing heart failure, COPD, and post-surgical recovery.<\/p>\r\n\r\n\r\n\r\n<p>Another great example is <strong>Luscii<\/strong>, which works with hospitals in Europe and uses AI to monitor vital signs remotely. It reduced emergency admissions by <strong>30%<\/strong> in a pilot program.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Reduces hospital visits and readmissions<\/li>\r\n\r\n\r\n\r\n<li>Allows care teams to intervene early<\/li>\r\n\r\n\r\n\r\n<li>Keeps chronic patients safer at home<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h3 id=\"h-predictive-analytics-for-patient-risk\" class=\"wp-block-heading\"><strong>Predictive Analytics for Patient Risk<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Imagine knowing who\u2019s likely to land in the ICU\u2014days before it happens. That\u2019s what AI is doing right now.<\/p>\r\n\r\n\r\n\r\n<p>Predictive analytics uses historical data\u2014like EMRs, lab results, medication history, and even social determinants of health\u2014to forecast patient risk. We&#8217;re not just talking about risk scores anymore. We\u2019re talking about real, actionable predictions.<\/p>\r\n\r\n\r\n\r\n<p>Cera Care, a UK-based home healthcare provider, uses AI to predict if an elderly patient is likely to fall sick within the next 7 days. With this tech, they cut emergency hospital visits by 52% and reduced care costs by 35%. Their model analyzes everything from blood pressure trends to behavioral patterns like missed meals or reduced movement.<\/p>\r\n\r\n\r\n\r\n<p>On the hospital side, Johns Hopkins developed a predictive AI system called PANDA that flagged early signs of sepsis up to 6 hours before symptoms were visible\u2014giving doctors a critical head start in treatment.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Helps care teams triage smarter<\/li>\r\n\r\n\r\n\r\n<li>Reduces avoidable hospitalizations<\/li>\r\n\r\n\r\n\r\n<li>Enables proactive care for high-risk patients<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<p>For apps handling chronic care, elder care, or post-discharge patients, predictive risk analytics can be a standout feature\u2014and a serious value-add.<\/p>\r\n\r\n\r\n\r\n<h3 id=\"h-medical-imaging-amp-diagnostics\" class=\"wp-block-heading\"><strong>Medical Imaging &amp; Diagnostics<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Medical imaging is one of the most advanced areas of AI in healthcare. AI models now analyze MRIs, CT scans, X-rays, and ultrasounds with incredible speed and accuracy. They&#8217;re flagging tumors, fractures, lung diseases, and even early signs of stroke\u2014all in real time.<\/p>\r\n\r\n\r\n\r\n<p>One standout example is <strong>Zebra Medical Vision<\/strong>. Their AI reads radiology scans and detects dozens of conditions, including breast cancer and brain bleeds. It has been deployed across hospitals in the U.S., Europe, and Asia. Zebra claims its AI can read scans <strong>60% faster<\/strong> than radiologists and has already processed <strong>over 2 million scans<\/strong> globally.<\/p>\r\n\r\n\r\n\r\n<p>Another is <strong>MONAI (Medical Open Network for AI)<\/strong>\u2014an open-source framework built specifically for medical imaging. It\u2019s used by developers to train custom imaging models, and hospitals use these models to detect issues that might not be caught during a rushed manual review.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Accelerates diagnostics<\/li>\r\n\r\n\r\n\r\n<li>Reduces human error<\/li>\r\n\r\n\r\n\r\n<li>Helps underserved areas with fewer radiologists<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h3 id=\"h-personalized-treatment-recommendations\" class=\"wp-block-heading\"><strong>Personalized Treatment Recommendations<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>One-size-fits-all doesn\u2019t work in healthcare\u2014and AI finally gives us a way to fix that.<\/p>\r\n\r\n\r\n\r\n<p>Personalized treatment is about more than just using a patient&#8217;s name. It\u2019s about using their unique data\u2014medical history, genetics, lifestyle, comorbidities\u2014to recommend treatments that are more likely to work for them.<\/p>\r\n\r\n\r\n\r\n<p>Take <strong>K Health<\/strong>, for example. Their AI-driven app analyzes thousands of similar patient cases to recommend treatments or direct users to doctors. The app uses data from over <strong>2 billion anonymized medical records<\/strong> and clinical guidelines. It\u2019s like having a doctor that\u2019s read every textbook <em>and<\/em> seen every case.<\/p>\r\n\r\n\r\n\r\n<p>Another major player is <strong>Tempus<\/strong>, which combines AI with genomic data to personalize cancer treatment. Their models suggest therapies based on how patients with similar genetic markers responded\u2014helping oncologists tailor their decisions.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Improves patient outcomes with data-backed decisions.<\/li>\r\n\r\n\r\n\r\n<li>Builds user trust through tailored care plans.<\/li>\r\n\r\n\r\n\r\n<li>Enables real-time updates to treatment as new data rolls in.<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h3 id=\"h-ai-driven-drug-discovery\" class=\"wp-block-heading\"><strong>AI-Driven Drug Discovery<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>AI is cutting years\u2014and millions of dollars\u2014out of drug development.<\/p>\r\n\r\n\r\n\r\n<p>Traditionally, bringing a new drug to market takes about <strong>10\u201315 years<\/strong> and can cost over <strong>$2.6 billion<\/strong>, according to the Tufts Center for the Study of Drug Development. AI is helping shave that timeline down by predicting which compounds are most likely to succeed\u2014before any human steps into a lab.<\/p>\r\n\r\n\r\n\r\n<p>Take <strong>Atomwise<\/strong>, for instance. They use AI to analyze molecular structures and predict how different chemicals will interact. Their algorithms can screen over <strong>16 billion compounds<\/strong> in days, speeding up the early discovery phase dramatically.<\/p>\r\n\r\n\r\n\r\n<p>There\u2019s also <strong>BenevolentAI<\/strong>, which identified a potential drug candidate for COVID-19 in <strong>just four days<\/strong>\u2014a process that typically takes months. The drug went into clinical trials almost immediately.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Shortens research timelines<\/li>\r\n\r\n\r\n\r\n<li>Lowers R&amp;D costs<\/li>\r\n\r\n\r\n\r\n<li>Helps smaller pharma and biotech startups stay competitive<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<p>If your healthtech product touches pharma, clinical research, or medtech, AI in drug discovery is a feature worth exploring\u2014or at least preparing for.<\/p>\r\n\r\n\r\n\r\n<h3 id=\"h-workflow-automation-e-g-scribing-billing\" class=\"wp-block-heading\"><strong>Workflow Automation (e.g. Scribing, Billing)<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Doctors didn\u2019t go to med school to spend half their day typing notes. But that\u2019s what most of them do.<\/p>\r\n\r\n\r\n\r\n<p>Healthcare professionals spend nearly <strong>50% of their time<\/strong> on documentation and administrative tasks, according to a study by the American Medical Association. That\u2019s where AI-driven workflow automation comes in.<\/p>\r\n\r\n\r\n\r\n<p><strong>Abridge<\/strong> is a perfect example. It\u2019s an AI medical scribe that listens to patient visits and generates clinical notes in real time. It can transcribe with <strong>over 90% accuracy<\/strong>, summarize key points, and even structure data for EMRs.<\/p>\r\n\r\n\r\n\r\n<p>Another one is <strong>Suki<\/strong>, which lets doctors dictate notes and uses AI to automatically organize them by SOAP format (Subjective, Objective, Assessment, Plan). Physicians using Suki report saving an average of <strong>1.5 hours per day<\/strong>.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Reduces physician burnout.<\/li>\r\n\r\n\r\n\r\n<li>Speeds up documentation and billing.<\/li>\r\n\r\n\r\n\r\n<li>Increases time spent with patients\u2014not paperwork.<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h3 id=\"h-mental-health-support\" class=\"wp-block-heading\"><strong>Mental Health Support<\/strong><\/h3>\r\n\r\n\r\n\r\n<p>Mental health needs help. AI is stepping in where the system falls short.<\/p>\r\n\r\n\r\n\r\n<p>With the global mental health crisis growing, apps are filling the care gap\u2014and AI is powering many of them. AI can track mood, detect early signs of depression, offer coping tools, and even simulate therapeutic conversations.<\/p>\r\n\r\n\r\n\r\n<p>Take <strong>Wysa<\/strong>, a mental health app that uses an AI chatbot trained in cognitive behavioral therapy (CBT). It\u2019s been downloaded over <strong>4.5 million times<\/strong> and has shown clinical effectiveness in reducing anxiety and depression symptoms. Users open up to the AI more often than to humans, thanks to the 24\/7 anonymity and zero judgment.<\/p>\r\n\r\n\r\n\r\n<p>Another example? <strong>Youper<\/strong>, which uses AI to guide users through emotional check-ins and deliver CBT interventions in real time. Studies show users experience a <strong>28% reduction in depressive symptoms<\/strong> within the first two weeks.<\/p>\r\n\r\n\r\n\r\n<p><strong>Why it works in apps:<\/strong><\/p>\r\n\r\n\r\n\r\n<ul class=\"wp-block-list\">\r\n<li>Scalable support for users who can\u2019t access therapy.<\/li>\r\n\r\n\r\n\r\n<li>Reduces the stigma of seeking help.<\/li>\r\n\r\n\r\n\r\n<li>Collects valuable behavioral data for personalized care.<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h2 id=\"h-integrate-ai-in-your-own-healthcare-app\" class=\"wp-block-heading\"><strong>Integrate AI in Your Own Healthcare App<\/strong><\/h2>\r\n\r\n\r\n\r\n<p><strong>You\u2019ve seen what AI can do. Now the real question is\u2014how do you build it into your own product?<\/strong><\/p>\r\n\r\n\r\n\r\n<p>Whether you\u2019re building from scratch or scaling an existing app, integrating AI isn\u2019t just about hiring a data scientist. You\u2019ll need the right architecture, clean and structured data, domain-specific models, and clinical validation. It&#8217;s a lot\u2014and most U.S. healthtech companies aren\u2019t staffed for it.<\/p>\r\n\r\n\r\n\r\n<p>This is why a growing number of U.S. healthtech companies are outsourcing AI development to India. Not for cost savings (though that helps), but because India has become a powerhouse for AI-backed healthcare solutions. Think 50,000+ AI engineers, strong clinical data annotation pipelines, and teams who already speak HL7, FHIR, and HIPAA.<\/p>\r\n\r\n\r\n\r\n<p>Engineerbabu is an <a href=\"https:\/\/engineerbabu.com\/\">IT service provider in India<\/a> that specializes in AI, machine learning, and healthcare compliance. Our development teams understand FHIR, HL7, ICD-10, and the FDA&#8217;s AI\/ML SaMD framework. They\u2019ve worked with U.S. companies, know HIPAA inside and out, and can help you ship features fast\u2014with fewer risks.<\/p>\r\n\r\n\r\n\r\n<h2 id=\"h-conclusion\" class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\r\n\r\n\r\n\r\n<p>AI in healthcare is no longer about experimentation\u2014it\u2019s about execution. From identifying high-risk patients before symptoms appear to reducing hospital readmissions through real-time monitoring, these use cases are solving real clinical problems.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>They&#8217;re improving outcomes, lowering costs, and closing the gaps in access and efficiency that healthcare has struggled with for years. For healthtech leaders, the opportunity now is to move beyond ideas and into implementation.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>That means understanding the right use cases, aligning them with patient and provider needs, and building with speed and clinical integrity. The teams who get this right won\u2019t just keep up\u2014they\u2019ll lead.<\/p>\r\n\r\n\r\n\r\n<h2 id=\"h-faqs\" class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\r\n\r\n\r\n\r\n<p><strong>How do I know which AI use case is right for my healthcare app?<\/strong><\/p>\r\n\r\n\r\n\r\n<p>Start by looking at the core problem you&#8217;re solving\u2014diagnosis, engagement, monitoring, or operations. Then assess your data quality, target user (patients or providers), and what decisions need automation. Match that with proven use cases already working in similar environments.<\/p>\r\n\r\n\r\n\r\n<p><strong>What are the key compliance requirements when building AI for healthcare?<\/strong><\/p>\r\n\r\n\r\n\r\n<p>If you&#8217;re in the U.S. market, HIPAA is non-negotiable. If your app handles clinical data, you may also need to consider HL7, FHIR, and FDA guidance on Software as a Medical Device (SaMD). Always bake compliance into your architecture early\u2014retrofits are expensive.<\/p>\r\n\r\n\r\n\r\n<p><strong>Can I outsource AI development and still meet HIPAA and security standards?<\/strong><\/p>\r\n\r\n\r\n\r\n<p>Yes, if your outsourcing partner has prior experience with healthcare applications. Ensure they have signed BAAs, use secure development environments, encrypt data at rest and in transit, and follow access control best practices. Vetting the team\u2019s healthcare track record is key.<\/p>\r\n\r\n\r\n\r\n<p><strong>What\u2019s the average timeline to build and deploy an AI feature in a healthcare app?<\/strong><\/p>\r\n\r\n\r\n\r\n<p>Simple AI features (like chatbots or appointment predictors) can go live in 4\u20136 weeks. More complex systems (like diagnostic AI or predictive analytics) may take 3\u20136 months, depending on data readiness, model training, and regulatory validation.<\/p>\r\n\r\n\r\n\r\n<p><strong>Can Engineerbabu build AI-powered healthcare apps for my company?<\/strong><\/p>\r\n\r\n\r\n\r\n<p>Yes. Engineerbabu is a healthcare app development company that connects you with top-tier engineering teams in India, experienced in building AI-driven features like diagnostics, remote monitoring, and workflow automation. These teams understand HIPAA, FHIR, HL7, and other healthcare standards. Whether you&#8217;re starting from scratch or upgrading an existing product, Engineerbabu helps you move fast with the right technical and compliance-ready expertise.<\/p>\r\n","protected":false},"excerpt":{"rendered":"<p>Here\u2019s something most founders don\u2019t like hearing: your healthcare app isn\u2019t slow because of dev speed, and it\u2019s not underperforming because of budget. It\u2019s probably just not smart enough yet. That\u2019s what the numbers are telling us. Healthcare apps that use AI are pulling ahead\u2014fast. They\u2019re reducing diagnostic errors by 30%, slashing patient no-show rates [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":19658,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1246],"tags":[],"class_list":["post-19657","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-healthtech"],"_links":{"self":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/19657","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/comments?post=19657"}],"version-history":[{"count":2,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/19657\/revisions"}],"predecessor-version":[{"id":21225,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/posts\/19657\/revisions\/21225"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/media\/19658"}],"wp:attachment":[{"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/media?parent=19657"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/categories?post=19657"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/engineerbabu.com\/blog\/wp-json\/wp\/v2\/tags?post=19657"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}