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See real-world use cases in healthcare and the nine connectivity features that turn promising AI models into regulated products.",{"tag":10,"attributes":19},{"name":20,"content":17},"abstract",{"tag":10,"attributes":22},{"name":23,"content":24},"icbm","51.525772, -0.082716",{"tag":26,"attributes":27},"link",{"rel":28,"href":29},"canonical","https:\u002F\u002Fcms.zuehlke.com\u002Fen\u002Finsights\u002Fconnectivity-is-defining-the-future-of-ai-in-medtech-and-healthcare",{"tag":10,"attributes":31},{"name":32,"content":33},"robots","index, 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Zühlke",{"tag":10,"attributes":67},{"property":68,"content":17},"og:description",{"tag":10,"attributes":70},{"property":71,"content":72},"og:image","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Fmax_1300x1300\u002Fpublic\u002Fimages\u002F5%201.png?itok=Dtalww5J",{"tag":10,"attributes":74},{"property":71,"content":75},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuhlke_innovation_social.jpg",{"tag":10,"attributes":77},{"property":78,"content":72},"og:image:url",{"tag":10,"attributes":80},{"property":78,"content":75},{"tag":10,"attributes":82},{"property":83,"content":72},"og:image:secure_url",{"tag":10,"attributes":85},{"property":83,"content":86},"https:\u002F\u002Fwww.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuhlke_innovation_social.jpg",{"tag":10,"attributes":88},{"property":89,"content":90},"og:image:alt","image",{"tag":10,"attributes":92},{"property":93,"content":94},"og:updated_time","2026-03-30",{"tag":10,"attributes":96},{"property":97,"content":98},"place:location:longitude","7.438014",{"tag":10,"attributes":100},{"property":101,"content":102},"place:location:latitude","46.947944",{"tag":10,"attributes":104},{"property":105,"content":106},"og:street_address","80 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is defining the future of AI in MedTech and healthcare",[206,209],{"path":203,"langcode":207},{"id":208},"en",{"path":210,"langcode":211},"\u002Fde\u002Finsights\u002Fmedical-grade-connectivity-als-basis-fuer-ki-im-gesundheitswesen",{"id":212},"de",{"id":214,"sharedMediaLegend":215,"mediaImage":216,"name":236},"173022",null,{"url":217,"width":218,"height":219,"alt":220,"title":215,"size":221,"mime":222,"variations":215,"headerMinimalVariations":223},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002F5%201.png",800,450,"Healthcare professionals discussing. ",42689,"image\u002Fpng",[224,228,229,232,235],{"name":225,"url":226,"width":227,"height":219},"FOCAL_POINT32_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002F5%201.png.webp?itok=PLCnX5SN",676,{"name":225,"url":226,"width":227,"height":219},{"name":230,"url":231,"width":218,"height":219},"FOCAL_POINT169_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_medium\u002Fpublic\u002Fimages\u002F5%201.png.webp?itok=70RJ7MUn",{"name":233,"url":234,"width":218,"height":219},"FOCAL_POINT169_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_large\u002Fpublic\u002Fimages\u002F5%201.png.webp?itok=Cx92pW5-",{"name":233,"url":234,"width":218,"height":219},"5 1.png",{"processed":238},"\u003Cp>AI in MedTech is advancing fast. See real-world use cases in healthcare and the nine connectivity features that turn promising AI models into regulated products.\u003C\u002Fp>",{"id":214,"sharedMediaLegend":215,"mediaImage":240,"name":236},{"url":217,"width":218,"height":219,"alt":220,"title":215,"size":221,"mime":222,"variations":215},[242,249,277,322,327,349,355,427,432,443],{"__typename":243,"id":244,"ctaLink":215,"pgSharedText":245,"pgSharedTitle":215,"pgSharedCtaLink":215},"ParagraphPgText","34481",{"value":246,"processed":247,"format":248},"\u003Cp>There are an abundance of use cases for artificial intelligence (AI) in \u003Ca href=\"\u002Fnode\u002F26275\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"5bb11bd1-0449-4cc6-95e0-c53bfa7cddbb\" data-entity-substitution=\"canonical\">healthcare\u003C\u002Fa>, and advances are being accelerated by using AI during development of novel MedTech. Commercial examples, however, are being held back by an execution gap. The challenge no longer lies in developing a smart algorithm or training a model, but in doing so in the context of a complete data flow ecosystem. \u003C\u002Fp>\u003Cp>AI in MedTech that can be scaled safely and economically requires a connectivity fabric that moves and processes protected health data between devices and the cloud securely, compliantly, and at speed. With today’s technological capabilities, this infrastructure is the difference between ideas that go nowhere and products that deliver genuine AI for clinical decision support and improved patient outcomes.\u003C\u002Fp>\u003Cp>This blog post examines impactful emerging AI use cases in healthcare and outlines why medical‑grade connectivity is the foundation for turning promising ideas into approved, scalable products. Whether you’re designing a new device, modernising a cloud data pipeline, or rethinking your real‑time data flows, the architecture you choose now determines how far your AI‑enabled MedTech solution can go in the future.\u003Cbr> \u003C\u002Fp>","\u003Cp>There are an abundance of use cases for artificial intelligence (AI) in \u003Ca href=\"\u002Fen\u002Findustries\u002Fhealthcare-providers\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"5bb11bd1-0449-4cc6-95e0-c53bfa7cddbb\" data-entity-substitution=\"canonical\" title=\"INDUSTRY: Healthcare providers (EN)\">healthcare\u003C\u002Fa>, and advances are being accelerated by using AI during development of novel MedTech. Commercial examples, however, are being held back by an execution gap. The challenge no longer lies in developing a smart algorithm or training a model, but in doing so in the context of a complete data flow ecosystem. \u003C\u002Fp>\u003Cp>AI in MedTech that can be scaled safely and economically requires a connectivity fabric that moves and processes protected health data between devices and the cloud securely, compliantly, and at speed. With today’s technological capabilities, this infrastructure is the difference between ideas that go nowhere and products that deliver genuine AI for clinical decision support and improved patient outcomes.\u003C\u002Fp>\u003Cp>This blog post examines impactful emerging AI use cases in healthcare and outlines why medical‑grade connectivity is the foundation for turning promising ideas into approved, scalable products. Whether you’re designing a new device, modernising a cloud data pipeline, or rethinking your real‑time data flows, the architecture you choose now determines how far your AI‑enabled MedTech solution can go in the future.\u003Cbr> \u003C\u002Fp>","full_html",{"__typename":250,"id":251,"caption":215,"imageRight":252,"pgSharedTitle":253,"pgSharedCtaLink":215,"textMedia":254,"pgSharedText":275},"ParagraphPgTextMedia","34482",false,"A growing spectrum of AI use cases in healthcare",{"id":255,"sharedMediaLegend":215,"mediaImage":256,"name":274},"173025",{"url":257,"width":258,"height":259,"alt":260,"title":215,"size":261,"mime":222,"variations":215,"textMediaVariations":262},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FAbstract%20Health%20AI%20Imagery%202026.png",1200,686,"\"\"",1089797,[263,268,269,270,273],{"name":264,"url":265,"width":266,"height":267},"FIXED_WIDTH_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002FAbstract%20Health%20AI%20Imagery%202026.png.webp?itok=8azfWScA",900,515,{"name":264,"url":265,"width":266,"height":267},{"name":264,"url":265,"width":266,"height":267},{"name":271,"url":272,"width":258,"height":259},"FIXED_WIDTH_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002FAbstract%20Health%20AI%20Imagery%202026.png.webp?itok=FDYkV23H",{"name":271,"url":272,"width":258,"height":259},"Abstract Health AI Imagery 2026.png",{"value":276,"processed":276,"format":248},"\u003Cp>The MedTech industry is rich with innovation. Developers are pushing the boundaries of AI for healthcare data analysis with robust, complex AI models, powerful on-device processors, and increasingly sophisticated sensing technology. The expanding spectrum of creativity is evident in the diversity of MedTech AI use cases.  \u003C\u002Fp>",{"__typename":278,"id":279,"title":280,"lead":215,"tabs":281},"ParagraphTab","34493","Where AI adds value to healthcare data analysis and clinical decision support",[282,290,298,306,314],{"infoText":283,"pgSharedParagraphs":284},"Wearable and continuous patient monitoring",[285],{"id":286,"ctaLink":215,"pgSharedText":287,"pgSharedTitle":283,"pgSharedCtaLink":215},"34483",{"value":288,"processed":289,"format":248},"\u003Cp>Wearable monitoring systems continuously capture physiological signals such as glucose levels, cardiac rhythms, or respiratory patterns. On‑device algorithms perform immediate filtering and anomaly detection, supporting timely responses during daily activities. Deeper clinical value emerges when data reaches the cloud, where population‑scale benchmarks and longitudinal histories reveal subtle trends, therapy effectiveness, and early signs of deterioration. These systems merge real‑time sensing with cloud‑based learning, enabling adaptive, proactive patient care. \u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e15e624973eb57854bf0493cb3acdca0f\">AI-ECG (\u003Ca href=\"https:\u002F\u002Falivecor.com\u002Four-ai\" target=\"_blank\">Kardia\u002FAliveCor\u003C\u002Fa>): Handheld or wearable devices that detect atrial fibrillation instantly. \u003C\u002Fli>\u003Cli data-list-item-id=\"e73e1796208268fe7d7124f5fc27fc903\">Closed-loop insulin therapy (\u003Ca href=\"https:\u002F\u002Fwww.mylife-diabetescare.com\u002Fen\u002Fmylife-loop.html\" target=\"_blank\">Ypsomed\u003C\u002Fa>): A mobile app that securely connects to and controls insulin pumps based on real-time data. \u003C\u002Fli>\u003Cli data-list-item-id=\"e2a272cf671c22a31ee8a3f4ac450a196\">False arrhythmia alert filtering (\u003Ca href=\"https:\u002F\u002Fwww.medtronic.com\u002Fen-us\u002Fhealthcare-professionals\u002Fproducts\u002Fcardiac-rhythm\u002Ftechnologies\u002Faccurhythm-ai-algorithms.html\" target=\"_blank\">Medtronic AccuRhythm\u003C\u002Fa>): On-device AI that filters out noise to reduce alarm fatigue for doctors. Also connects to the cloud.\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>Wearable monitoring systems continuously capture physiological signals such as glucose levels, cardiac rhythms, or respiratory patterns. On‑device algorithms perform immediate filtering and anomaly detection, supporting timely responses during daily activities. Deeper clinical value emerges when data reaches the cloud, where population‑scale benchmarks and longitudinal histories reveal subtle trends, therapy effectiveness, and early signs of deterioration. These systems merge real‑time sensing with cloud‑based learning, enabling adaptive, proactive patient care. \u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli>AI-ECG (\u003Ca href=\"https:\u002F\u002Falivecor.com\u002Four-ai\" target=\"_blank\">Kardia\u002FAliveCor\u003C\u002Fa>): Handheld or wearable devices that detect atrial fibrillation instantly. \u003C\u002Fli>\u003Cli>Closed-loop insulin therapy (\u003Ca href=\"https:\u002F\u002Fwww.mylife-diabetescare.com\u002Fen\u002Fmylife-loop.html\" target=\"_blank\">Ypsomed\u003C\u002Fa>): A mobile app that securely connects to and controls insulin pumps based on real-time data. \u003C\u002Fli>\u003Cli>False arrhythmia alert filtering (\u003Ca href=\"https:\u002F\u002Fwww.medtronic.com\u002Fen-us\u002Fhealthcare-professionals\u002Fproducts\u002Fcardiac-rhythm\u002Ftechnologies\u002Faccurhythm-ai-algorithms.html\" target=\"_blank\">Medtronic AccuRhythm\u003C\u002Fa>): On-device AI that filters out noise to reduce alarm fatigue for doctors. Also connects to the cloud.\u003C\u002Fli>\u003C\u002Ful>",{"infoText":291,"pgSharedParagraphs":292},"Diagnostic imaging and pathology",[293],{"id":294,"ctaLink":215,"pgSharedText":295,"pgSharedTitle":291,"pgSharedCtaLink":215},"34485",{"value":296,"processed":297,"format":248},"\u003Cp>Imaging and pathology workflows blend high‑precision image acquisition with powerful off‑device interpretation. Safety‑critical checks and initial compressions happen at the device or scanner, while advanced analysis, including segmentation, grading, triage, and multi‑modal fusion, is carried out in cloud environments built for heavy compute. Consistent connectivity allows images and metadata to be processed, compared, and refined at scale, supporting more uniform diagnostic quality. Continuous learning pipelines further improve models and interpretation without requiring hardware changes. \u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"ea9211f3ab6a98e177f0394e5d1fc0d26\">Digital slide cancer detection (\u003Ca href=\"https:\u002F\u002Fwww.philips.com\u002Fa-w\u002Fabout\u002Fnews\u002Farchive\u002Fstandard\u002Fnews\u002Fpress\u002F2025\u002Fphilips-and-ibex-expand-partnership-to-enhance-ai-enabled-pathology-workflows-for-better-patient-care.html\" target=\"_blank\">Philips\u002FIbex\u003C\u002Fa>): Automated analysis of pathology slides on local hospital servers to assist with cancer diagnosis. Also offers cloud-based archiving services. \u003C\u002Fli>\u003Cli data-list-item-id=\"e87b907b17cc7c84d0f3db19815941436\">Skin disorder classification (\u003Ca href=\"https:\u002F\u002Faisencia.de\u002Fen\u002F\" target=\"_blank\">aisencia\u003C\u002Fa>): Tools to help pathologists classify 40 different skin disorders locally, reducing wait times.\u003C\u002Fli>\u003Cli data-list-item-id=\"e144f902325e686b13d286ab6d929cafb\">Patient screening and stratification (\u003Ca href=\"https:\u002F\u002Filof.tech\u002F\" target=\"_blank\">iLoF\u003C\u002Fa>): Photonic blood analysis that uses AI to create a unique digital molecular profile.\u003C\u002Fli>\u003Cli data-list-item-id=\"e533454f880244e64cb8eec2ec17e8cb1\">Rapid stroke detection and other image-based analysis (\u003Ca href=\"https:\u002F\u002Fwww.viz.ai\u002F\" target=\"_blank\">Viz.ai\u003C\u002Fa> \u002F \u003Ca href=\"https:\u002F\u002Fwww.aidoc.com\u002Fsolutions\u002Fradiology\u002F\" target=\"_blank\">Aidoc\u003C\u002Fa>): AI analyses CT scans at the hospital and with cloud-based loop to alert specialists within minutes of the scan.\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>Imaging and pathology workflows blend high‑precision image acquisition with powerful off‑device interpretation. Safety‑critical checks and initial compressions happen at the device or scanner, while advanced analysis, including segmentation, grading, triage, and multi‑modal fusion, is carried out in cloud environments built for heavy compute. Consistent connectivity allows images and metadata to be processed, compared, and refined at scale, supporting more uniform diagnostic quality. Continuous learning pipelines further improve models and interpretation without requiring hardware changes. \u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli>Digital slide cancer detection (\u003Ca href=\"https:\u002F\u002Fwww.philips.com\u002Fa-w\u002Fabout\u002Fnews\u002Farchive\u002Fstandard\u002Fnews\u002Fpress\u002F2025\u002Fphilips-and-ibex-expand-partnership-to-enhance-ai-enabled-pathology-workflows-for-better-patient-care.html\" target=\"_blank\">Philips\u002FIbex\u003C\u002Fa>): Automated analysis of pathology slides on local hospital servers to assist with cancer diagnosis. Also offers cloud-based archiving services. \u003C\u002Fli>\u003Cli>Skin disorder classification (\u003Ca href=\"https:\u002F\u002Faisencia.de\u002Fen\u002F\" target=\"_blank\">aisencia\u003C\u002Fa>): Tools to help pathologists classify 40 different skin disorders locally, reducing wait times.\u003C\u002Fli>\u003Cli>Patient screening and stratification (\u003Ca href=\"https:\u002F\u002Filof.tech\u002F\" target=\"_blank\">iLoF\u003C\u002Fa>): Photonic blood analysis that uses AI to create a unique digital molecular profile.\u003C\u002Fli>\u003Cli>Rapid stroke detection and other image-based analysis (\u003Ca href=\"https:\u002F\u002Fwww.viz.ai\u002F\" target=\"_blank\">Viz.ai\u003C\u002Fa> \u002F \u003Ca href=\"https:\u002F\u002Fwww.aidoc.com\u002Fsolutions\u002Fradiology\u002F\" target=\"_blank\">Aidoc\u003C\u002Fa>): AI analyses CT scans at the hospital and with cloud-based loop to alert specialists within minutes of the scan.\u003C\u002Fli>\u003C\u002Ful>",{"infoText":299,"pgSharedParagraphs":300},"Surgical and procedural guidance",[301],{"id":302,"ctaLink":215,"pgSharedText":303,"pgSharedTitle":299,"pgSharedCtaLink":215},"34487",{"value":304,"processed":305,"format":248},"\u003Cp>AI-assisted surgical systems rely on tightly coupled on‑device control loops during procedures, ensuring precision, stability, and rapid responses to surgeon inputs. Beyond the operating room, cloud‑based intelligence enriches planning, simulation, and postoperative analysis. By combining real‑time intraoperative feedback with population‑level insights, these systems deploy new predictive algorithms, help personalise surgical strategies, and optimise guidance tools to continually raise the standard of procedural care. \u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"eed6833b61ca2840572523f4363e40c35\">Precision orthopaedic planning (\u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC10492632\u002F\" target=\"_blank\">Stryker Mako\u003C\u002Fa>): Robotic-arm assisted surgery providing real-time tactile and visual guidance for joint replacements. \u003C\u002Fli>\u003Cli data-list-item-id=\"e47e88f8bfa68578efc1663ccdc219d04\">Polyp detection (\u003Ca href=\"https:\u002F\u002Fwww.medtronic.com\u002Fen-ie\u002Fc\u002Fhealthcare-professionals\u002Fendoscopy\u002Fgi-genius-colonpro.html\" target=\"_blank\">Medtronic GI Genius\u003C\u002Fa>): A hardware box in the endoscopy suite that highlights potential polyps in real-time video feeds.\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>AI-assisted surgical systems rely on tightly coupled on‑device control loops during procedures, ensuring precision, stability, and rapid responses to surgeon inputs. Beyond the operating room, cloud‑based intelligence enriches planning, simulation, and postoperative analysis. By combining real‑time intraoperative feedback with population‑level insights, these systems deploy new predictive algorithms, help personalise surgical strategies, and optimise guidance tools to continually raise the standard of procedural care. \u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli>Precision orthopaedic planning (\u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC10492632\u002F\" target=\"_blank\">Stryker Mako\u003C\u002Fa>): Robotic-arm assisted surgery providing real-time tactile and visual guidance for joint replacements. \u003C\u002Fli>\u003Cli>Polyp detection (\u003Ca href=\"https:\u002F\u002Fwww.medtronic.com\u002Fen-ie\u002Fc\u002Fhealthcare-professionals\u002Fendoscopy\u002Fgi-genius-colonpro.html\" target=\"_blank\">Medtronic GI Genius\u003C\u002Fa>): A hardware box in the endoscopy suite that highlights potential polyps in real-time video feeds.\u003C\u002Fli>\u003C\u002Ful>",{"infoText":307,"pgSharedParagraphs":308},"Clinical decision support and hospital operations",[309],{"id":310,"ctaLink":215,"pgSharedText":311,"pgSharedTitle":307,"pgSharedCtaLink":215},"34489",{"value":312,"processed":313,"format":248},"\u003Cp>Decision‑support platforms aggregate clinical, operational, and environmental data from across the care continuum. On‑premise systems provide immediate context on patient acuity, staffing levels, and bed status, while cloud engines synthesise broader patterns such as predicted operational surges, care delays, or resource bottlenecks. This combination enables real‑time guidance for clinicians and administrators, improving both patient flow and care quality. Continuous data loops and analysis enable these systems to reflect live conditions and to evolve through new predictive models and workflow optimisation.\u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e9b2e390f3db809cb916a6bb1954b778f\">Ambient Intelligence (\u003Ca href=\"https:\u002F\u002Fwww.stryker.com\u002Fus\u002Fen\u002Facute-care\u002Fc\u002Fcare-ai-human-centered-care.html\" target=\"_blank\">Care.ai\u002FStryker\u003C\u002Fa>): Smart hospital rooms that use local computer vision to monitor patient movement and prevent falls. \u003C\u002Fli>\u003Cli data-list-item-id=\"ef496f596bca660c449d6bb475477e429\">Sepsis early warning (\u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC10317482\" target=\"_blank\">Epic\u003C\u002Fa>\u002F\u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC7025344\" target=\"_blank\">Cerner\u003C\u002Fa>): AI integrated into hospital EHR systems that monitors vital signs 24\u002F7 to predict sepsis onset. \u003C\u002Fli>\u003Cli data-list-item-id=\"e6492ad1a67357d08db14b7071139359b\">Mental health triage (\u003Ca href=\"https:\u002F\u002Fwoebothealth.com\u002F\" target=\"_blank\">Woebot\u003C\u002Fa>): A cloud-based platform providing evidence-based support and triage for over 300,000 users. \u003C\u002Fli>\u003Cli data-list-item-id=\"e077cdfd6dc4aa868cd12e15eb365b38e\">Health operations (\u003Ca href=\"https:\u002F\u002Fwww.gehealthcare.com\u002Fproducts\u002Fsoftware\u002Fcommandcenter\" target=\"_blank\">GE Command Center\u003C\u002Fa>): Managing patient triage and bed capacity across an entire regional hospital network.\u003C\u002Fli>\u003Cli data-list-item-id=\"e1ebb466505912676e9194ade3356107f\">Digital cognitive therapeutics (\u003Ca href=\"https:\u002F\u002Fwww.akiliinteractive.com\u002Fscience-and-technology\" target=\"_blank\">Akili Interactive\u003C\u002Fa>): FDA-cleared video games for ADHD treatment delivered and monitored via a cloud platform.\u003C\u002Fli>\u003Cli data-list-item-id=\"efa172f1bbd6ea002260f339ca595ffe9\">Improving stroke diagnosis and care (\u003Ca href=\"https:\u002F\u002Fumbrella-ihi.eu\u002F\" target=\"_blank\">UMBRELLA\u003C\u002Fa>): European consortium automating the stroke diagnosis and care cycle across multiple regions.\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>Decision‑support platforms aggregate clinical, operational, and environmental data from across the care continuum. On‑premise systems provide immediate context on patient acuity, staffing levels, and bed status, while cloud engines synthesise broader patterns such as predicted operational surges, care delays, or resource bottlenecks. This combination enables real‑time guidance for clinicians and administrators, improving both patient flow and care quality. Continuous data loops and analysis enable these systems to reflect live conditions and to evolve through new predictive models and workflow optimisation.\u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli>Ambient Intelligence (\u003Ca href=\"https:\u002F\u002Fwww.stryker.com\u002Fus\u002Fen\u002Facute-care\u002Fc\u002Fcare-ai-human-centered-care.html\" target=\"_blank\">Care.ai\u002FStryker\u003C\u002Fa>): Smart hospital rooms that use local computer vision to monitor patient movement and prevent falls. \u003C\u002Fli>\u003Cli>Sepsis early warning (\u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC10317482\" target=\"_blank\">Epic\u003C\u002Fa>\u002F\u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC7025344\" target=\"_blank\">Cerner\u003C\u002Fa>): AI integrated into hospital EHR systems that monitors vital signs 24\u002F7 to predict sepsis onset. \u003C\u002Fli>\u003Cli>Mental health triage (\u003Ca href=\"https:\u002F\u002Fwoebothealth.com\u002F\" target=\"_blank\">Woebot\u003C\u002Fa>): A cloud-based platform providing evidence-based support and triage for over 300,000 users. \u003C\u002Fli>\u003Cli>Health operations (\u003Ca href=\"https:\u002F\u002Fwww.gehealthcare.com\u002Fproducts\u002Fsoftware\u002Fcommandcenter\" target=\"_blank\">GE Command Center\u003C\u002Fa>): Managing patient triage and bed capacity across an entire regional hospital network.\u003C\u002Fli>\u003Cli>Digital cognitive therapeutics (\u003Ca href=\"https:\u002F\u002Fwww.akiliinteractive.com\u002Fscience-and-technology\" target=\"_blank\">Akili Interactive\u003C\u002Fa>): FDA-cleared video games for ADHD treatment delivered and monitored via a cloud platform.\u003C\u002Fli>\u003Cli>Improving stroke diagnosis and care (\u003Ca href=\"https:\u002F\u002Fumbrella-ihi.eu\u002F\" target=\"_blank\">UMBRELLA\u003C\u002Fa>): European consortium automating the stroke diagnosis and care cycle across multiple regions.\u003C\u002Fli>\u003C\u002Ful>",{"infoText":315,"pgSharedParagraphs":316},"AI research, simulation and trial support",[317],{"id":318,"ctaLink":215,"pgSharedText":319,"pgSharedTitle":315,"pgSharedCtaLink":215},"34491",{"value":320,"processed":321,"format":248},"\u003Cp>Simulation and research tools use patient‑specific data to model anatomical behaviour, device performance, or treatment responses. Lightweight calculations can be run locally to support quick checks, but the full fidelity of virtual trials and probabilistic modelling depends on cloud‑scale compute. These platforms help teams explore therapy options, optimise device designs, and accelerate clinical studies with richer evidence. Each platform builds on secure data exchange, reproducibility, and the continuous refinement of models as new datasets and clinical insights emerge.\u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e6219e3ac4352d4379c41f4ba8bd90c26\">Predicting clinical outcomes of lung disease (\u003Ca href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-021-01506-3\" target=\"_blank\">EXAM Study\u003C\u002Fa> \u002F \u003Ca href=\"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F39455061\u002F\" target=\"_blank\">RACOON\u003C\u002Fa>): Connecting dozens of hospitals (e.g. 36 for RACOON) to train AI on lung diseases like COVID without transferring patient data. \u003C\u002Fli>\u003Cli data-list-item-id=\"e4047f455566318f9c2546cb248e8d7b3\">Multi-cancer imaging evaluation (\u003Ca href=\"https:\u002F\u002Feucanimage.eu\u002F\" target=\"_blank\">EuCanImage\u003C\u002Fa>): A European platform for scalable, GDPR-compliant imaging data deployment for generalisable AI training.\u003C\u002Fli>\u003Cli data-list-item-id=\"e8f0fb5085b7324173f3ed613e998470b\">Digital twins to plan interventions (\u003Ca href=\"https:\u002F\u002Fwww.feops.com\u002F\" target=\"_blank\">FEops\u003C\u002Fa> \u003Ca href=\"https:\u002F\u002Fwww.feops.com\u002F\" target=\"_blank\">HEARTGuide\u003C\u002Fa>™) and serve as controls in clinical trials (\u003Ca href=\"https:\u002F\u002Fwww.unlearn.ai\u002F\" target=\"_blank\">Unlearn AI\u003C\u002Fa>): The former creates pre-surgery computer simulations of how cardiovascular devices will interact with a patient’s cardiac anatomy based on CT scans. The latter produces AI-generated forecasts of trial participants’ expected control outcomes.\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>Simulation and research tools use patient‑specific data to model anatomical behaviour, device performance, or treatment responses. Lightweight calculations can be run locally to support quick checks, but the full fidelity of virtual trials and probabilistic modelling depends on cloud‑scale compute. These platforms help teams explore therapy options, optimise device designs, and accelerate clinical studies with richer evidence. Each platform builds on secure data exchange, reproducibility, and the continuous refinement of models as new datasets and clinical insights emerge.\u003C\u002Fp>\u003Cp>Examples: \u003C\u002Fp>\u003Cul>\u003Cli>Predicting clinical outcomes of lung disease (\u003Ca href=\"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-021-01506-3\" target=\"_blank\">EXAM Study\u003C\u002Fa> \u002F \u003Ca href=\"https:\u002F\u002Fpubmed.ncbi.nlm.nih.gov\u002F39455061\u002F\" target=\"_blank\">RACOON\u003C\u002Fa>): Connecting dozens of hospitals (e.g. 36 for RACOON) to train AI on lung diseases like COVID without transferring patient data. \u003C\u002Fli>\u003Cli>Multi-cancer imaging evaluation (\u003Ca href=\"https:\u002F\u002Feucanimage.eu\u002F\" target=\"_blank\">EuCanImage\u003C\u002Fa>): A European platform for scalable, GDPR-compliant imaging data deployment for generalisable AI training.\u003C\u002Fli>\u003Cli>Digital twins to plan interventions (\u003Ca href=\"https:\u002F\u002Fwww.feops.com\u002F\" target=\"_blank\">FEops\u003C\u002Fa> \u003Ca href=\"https:\u002F\u002Fwww.feops.com\u002F\" target=\"_blank\">HEARTGuide\u003C\u002Fa>™) and serve as controls in clinical trials (\u003Ca href=\"https:\u002F\u002Fwww.unlearn.ai\u002F\" target=\"_blank\">Unlearn AI\u003C\u002Fa>): The former creates pre-surgery computer simulations of how cardiovascular devices will interact with a patient’s cardiac anatomy based on CT scans. The latter produces AI-generated forecasts of trial participants’ expected control outcomes.\u003C\u002Fli>\u003C\u002Ful>",{"__typename":243,"id":323,"ctaLink":215,"pgSharedText":324,"pgSharedTitle":215,"pgSharedCtaLink":215},"34494",{"value":325,"processed":326,"format":248},"\u003Cp>All of the above examples share one thing in common – \u003Ca href=\"\u002Fnode\u002F6422\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"e47e9f54-d399-414e-be6d-bb0c366d68fe\" data-entity-substitution=\"canonical\">cloud-scale analytics\u003C\u002Fa> deliver intelligent adaptation to patient condition, benchmarking to real-world population parameters, and continuously updated decision loops. These advanced capabilities require medical-grade connectivity.\u003C\u002Fp>","\u003Cp>All of the above examples share one thing in common – \u003Ca href=\"\u002Fen\u002Fexpertise\u002Fcloud-transformation\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"e47e9f54-d399-414e-be6d-bb0c366d68fe\" data-entity-substitution=\"canonical\" title=\"EXPERTISE: Cloud (EN)\">cloud-scale analytics\u003C\u002Fa> deliver intelligent adaptation to patient condition, benchmarking to real-world population parameters, and continuously updated decision loops. These advanced capabilities require medical-grade connectivity.\u003C\u002Fp>",{"__typename":250,"id":328,"caption":215,"imageRight":252,"pgSharedTitle":329,"pgSharedCtaLink":215,"textMedia":330,"pgSharedText":346},"34495","Connectivity powers AI for clinical decision support",{"id":331,"sharedMediaLegend":215,"mediaImage":332,"name":345},"173026",{"url":333,"width":334,"height":335,"alt":260,"title":215,"size":336,"mime":222,"variations":215,"textMediaVariations":337},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FHealth%20AI%20Imagery%202026.png",608,816,711340,[338,340,341,342,344],{"name":264,"url":339,"width":334,"height":335},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002FHealth%20AI%20Imagery%202026.png.webp?itok=zNkA065f",{"name":264,"url":339,"width":334,"height":335},{"name":264,"url":339,"width":334,"height":335},{"name":271,"url":343,"width":334,"height":335},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002FHealth%20AI%20Imagery%202026.png.webp?itok=6874MHV8",{"name":271,"url":343,"width":334,"height":335},"Health AI Imagery 2026.png",{"value":347,"processed":348,"format":248},"\u003Cp>For AI in healthcare, developing novel AI models and \u003Ca href=\"\u002Fnode\u002F6657\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"5eabedb3-f1ce-4ba4-8c31-bbb379c6f0ce\" data-entity-substitution=\"canonical\">engineering smart devices\u003C\u002Fa> is no longer a bottleneck. The real issue delaying full-value deployment of MedTech AI today is execution – meeting data and processing requirements for these technologies. Breakthroughs in this space depend on complementing AI models with fit-for-purpose data ingestion, security‑compliant data transmission and storage, accurate integration with electronic health records, and rapid turnover of insights back to devices, all while ensuring full regulatory traceability. \u003C\u002Fp>\u003Cp>Medical-grade connectivity plugs this execution gap. The right connectivity architecture is designed to capture and orchestrate all end-to-end data flow steps, from secure device boot and identity, to operational data streaming, cloud ingestion and transformation, real‑time analytics, and comprehensive audit and data governance. If any single link in this flow is weak, your AI value chain is weak.\u003C\u002Fp>","\u003Cp>For AI in healthcare, developing novel AI models and \u003Ca href=\"\u002Fen\u002Fexpertise\u002Fhardware-engineering\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"5eabedb3-f1ce-4ba4-8c31-bbb379c6f0ce\" data-entity-substitution=\"canonical\" title=\"EXPERTISE: Hardware engineering (EN)\">engineering smart devices\u003C\u002Fa> is no longer a bottleneck. The real issue delaying full-value deployment of MedTech AI today is execution – meeting data and processing requirements for these technologies. Breakthroughs in this space depend on complementing AI models with fit-for-purpose data ingestion, security‑compliant data transmission and storage, accurate integration with electronic health records, and rapid turnover of insights back to devices, all while ensuring full regulatory traceability. \u003C\u002Fp>\u003Cp>Medical-grade connectivity plugs this execution gap. The right connectivity architecture is designed to capture and orchestrate all end-to-end data flow steps, from secure device boot and identity, to operational data streaming, cloud ingestion and transformation, real‑time analytics, and comprehensive audit and data governance. If any single link in this flow is weak, your AI value chain is weak.\u003C\u002Fp>",{"__typename":243,"id":350,"ctaLink":215,"pgSharedText":351,"pgSharedTitle":354,"pgSharedCtaLink":215},"34496",{"value":352,"processed":353,"format":248},"\u003Cp>Historically, connectivity architectures for AI in MedTech were constrained by the trade-off between data transmission speed and computational power. To ensure deterministic behaviour and ultra‑low latency in turnaround (a non‑negotiable requirement for patient safety), algorithms often stayed on device. Operating locally, however, meant foregoing the analytical power available to cloud-based systems.\u003C\u002Fp>\u003Cp>Today, that trade-off has vanished. Modern networks, protocols, and onsite runtimes are sufficiently resilient that real‑time responsiveness and cloud‑scale analytics are no longer mutually exclusive. Modern connectivity is reliable and secure, enabling MedTech AI products that merge local data processing with live cloud-scale intelligence.\u003C\u002Fp>\u003Cp>As a result, design priorities for data transmission, processing, and storage solutions have changed. The question is no longer about the distance data needs to travel, but rather:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e87a75fe6e628e2a7bf5170ddf1c4e9d7\">\u003Cstrong>Data sovereignty\u003C\u002Fstrong>: How can \u003Ca href=\"\u002Fnode\u002F26816\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"5288f1e3-ef0c-4090-8bcd-416bb43f98f9\" data-entity-substitution=\"canonical\">digital sovereignty\u003C\u002Fa> be guaranteed?\u003C\u002Fli>\u003Cli data-list-item-id=\"e08d180750ce7a292eb3a8ef0112e5d10\">\u003Cstrong>Governance\u003C\u002Fstrong>: When and how are identity, access, and usage controlled?\u003C\u002Fli>\u003Cli data-list-item-id=\"e0c4f9325e101e4e89d05cb441a410839\">\u003Cstrong>Consumption economics\u003C\u002Fstrong>: What’s the best way of balancing the costs of streaming, storing, and processing data at scale?\u003C\u002Fli>\u003C\u002Ful>\u003Cp>These drivers demand architectures that are intentionally designed for efficient performance, cost-aware data paths, and strict data privacy and compliance. This is the essence of medical-grade connectivity. \u003C\u002Fp>","\u003Cp>Historically, connectivity architectures for AI in MedTech were constrained by the trade-off between data transmission speed and computational power. To ensure deterministic behaviour and ultra‑low latency in turnaround (a non‑negotiable requirement for patient safety), algorithms often stayed on device. Operating locally, however, meant foregoing the analytical power available to cloud-based systems.\u003C\u002Fp>\u003Cp>Today, that trade-off has vanished. Modern networks, protocols, and onsite runtimes are sufficiently resilient that real‑time responsiveness and cloud‑scale analytics are no longer mutually exclusive. Modern connectivity is reliable and secure, enabling MedTech AI products that merge local data processing with live cloud-scale intelligence.\u003C\u002Fp>\u003Cp>As a result, design priorities for data transmission, processing, and storage solutions have changed. The question is no longer about the distance data needs to travel, but rather:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Data sovereignty\u003C\u002Fstrong>: How can \u003Ca href=\"\u002Fen\u002Finsights\u002Fdigital-sovereignty-guide\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"5288f1e3-ef0c-4090-8bcd-416bb43f98f9\" data-entity-substitution=\"canonical\" title=\"A strategic guide to digital sovereignty\">digital sovereignty\u003C\u002Fa> be guaranteed?\u003C\u002Fli>\u003Cli>\u003Cstrong>Governance\u003C\u002Fstrong>: When and how are identity, access, and usage controlled?\u003C\u002Fli>\u003Cli>\u003Cstrong>Consumption economics\u003C\u002Fstrong>: What’s the best way of balancing the costs of streaming, storing, and processing data at scale?\u003C\u002Fli>\u003C\u002Ful>\u003Cp>These drivers demand architectures that are intentionally designed for efficient performance, cost-aware data paths, and strict data privacy and compliance. This is the essence of medical-grade connectivity. \u003C\u002Fp>","New connectivity capabilities set the pace for AI in MedTech",{"__typename":278,"id":356,"title":357,"lead":358,"tabs":359},"34515","The nine features of medical-grade connectivity","Building generative or other forms of AI into medical devices starts with a clinical problem to solve, a signal to interpret, and a prediction to make. Transforming this concept into a reliable, regulated, scalable product requires a connectivity architecture capable of sustaining the entire data lifecycle. ",[360,368,376,383,391,399,406,413,420],{"infoText":361,"pgSharedParagraphs":362},"1. Regulatory and compliance foundation",[363],{"id":364,"ctaLink":215,"pgSharedText":365,"pgSharedTitle":361,"pgSharedCtaLink":215},"34497",{"value":366,"processed":367,"format":248},"\u003Cp>Connected medical devices operate in one of the most highly regulated environments in the world. Connectivity components have to comply with strict medical device regulations and quality management standards, and often require separate approval. \u003Ca href=\"\u002Fnode\u002F26712\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"94473c7c-0322-4ffa-a06d-4d5f01dbfbc2\" data-entity-substitution=\"canonical\">Building on a robust regulatory foundation\u003C\u002Fa> ensures that all connectivity capabilities are safe, auditable, and compliant throughout the device’s lifecycle.\u003C\u002Fp>","\u003Cp>Connected medical devices operate in one of the most highly regulated environments in the world. Connectivity components have to comply with strict medical device regulations and quality management standards, and often require separate approval. \u003Ca href=\"\u002Fen\u002Fabout-us\u002Fnews\u002Fzuhlke-and-confinis-launch-beyond-to-power-regulatory-compliance-in-the-age-of-ai\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"94473c7c-0322-4ffa-a06d-4d5f01dbfbc2\" data-entity-substitution=\"canonical\" title=\"Zühlke and Confinis launch 'Beyond' to power regulatory compliance in the age of AI \">Building on a robust regulatory foundation\u003C\u002Fa> ensures that all connectivity capabilities are safe, auditable, and compliant throughout the device’s lifecycle.\u003C\u002Fp>",{"infoText":369,"pgSharedParagraphs":370},"2. Security architecture framework",[371],{"id":372,"ctaLink":215,"pgSharedText":373,"pgSharedTitle":369,"pgSharedCtaLink":215},"34499",{"value":374,"processed":375,"format":248},"\u003Cp>A medical-grade approach to \u003Ca href=\"\u002Fnode\u002F6649\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"a5fe387a-19f8-4216-a8b5-3a4deeae15d4\" data-entity-substitution=\"canonical\">cybersecurity\u003C\u002Fa> means incorporating strong, multi-layered safety features, from secure communication and hardware security modules to continuous vulnerability management and encryption, at every stage. These measures protect patients, providers, and your business from cybersecurity threats that could jeopardise safety and trust.\u003C\u002Fp>","\u003Cp>A medical-grade approach to \u003Ca href=\"\u002Fen\u002Fexpertise\u002Fcybersecurity-consulting\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"a5fe387a-19f8-4216-a8b5-3a4deeae15d4\" data-entity-substitution=\"canonical\" title=\"EXPERTISE: Cybersecurity (EN)\">cybersecurity\u003C\u002Fa> means incorporating strong, multi-layered safety features, from secure communication and hardware security modules to continuous vulnerability management and encryption, at every stage. These measures protect patients, providers, and your business from cybersecurity threats that could jeopardise safety and trust.\u003C\u002Fp>",{"infoText":377,"pgSharedParagraphs":378},"3. Cloud platform architecture",[379],{"id":380,"ctaLink":215,"pgSharedText":381,"pgSharedTitle":377,"pgSharedCtaLink":215},"34501",{"value":382,"processed":382,"format":248},"\u003Cp>A reliable cloud architecture is essential for large-scale connectivity. Medical-grade platforms need to combine encrypted data storage, high availability infrastructure, and support for multiple communication protocols while still delivering low latency performance for critical alerts. Purpose-built data models and analytics capabilities ensure that MedTech devices remain responsive and clinically useful in real time.\u003C\u002Fp>",{"infoText":384,"pgSharedParagraphs":385},"4. Device management and monitoring",[386],{"id":387,"ctaLink":215,"pgSharedText":388,"pgSharedTitle":384,"pgSharedCtaLink":215},"34503",{"value":389,"processed":390,"format":248},"\u003Cp>Once devices are deployed, they need to be maintained, monitored, and updated without disrupting clinical operations. \u003Ca href=\"\u002Fnode\u002F26482\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"f991d7a4-0d57-4189-8616-e2314017ae57\" data-entity-substitution=\"canonical\">Medical-grade device management\u003C\u002Fa> includes secure over-the-air updates, full lifecycle tracking, real time health monitoring, and digital twins to represent each device’s current state. These capabilities help prevent downtime, reduce field issues, and maintain patient safety.\u003C\u002Fp>","\u003Cp>Once devices are deployed, they need to be maintained, monitored, and updated without disrupting clinical operations. \u003Ca href=\"\u002Fen\u002Finsights\u002Fthe-medtech-memory-lapse-in-product-development\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"f991d7a4-0d57-4189-8616-e2314017ae57\" data-entity-substitution=\"canonical\" title=\"The MedTech memory lapse in product development\">Medical-grade device management\u003C\u002Fa> includes secure over-the-air updates, full lifecycle tracking, real time health monitoring, and digital twins to represent each device’s current state. These capabilities help prevent downtime, reduce field issues, and maintain patient safety.\u003C\u002Fp>",{"infoText":392,"pgSharedParagraphs":393},"5. Interoperability and standards",[394],{"id":395,"ctaLink":215,"pgSharedText":396,"pgSharedTitle":392,"pgSharedCtaLink":215},"34505",{"value":397,"processed":398,"format":248},"\u003Cp>Healthcare systems rely on a broad ecosystem of software, imaging systems, and workflows. Medical-grade connectivity ensures devices speak the same language by using established \u003Ca href=\"\u002Fnode\u002F6647\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"685fea0a-4ce0-4b5d-ba0f-6fb137f51971\" data-entity-substitution=\"canonical\">clinical data standards and protocols\u003C\u002Fa>. This interoperability makes it easier for hospitals to adopt your devices and integrate them into their existing environment – reducing friction and improving clinical value.\u003C\u002Fp>","\u003Cp>Healthcare systems rely on a broad ecosystem of software, imaging systems, and workflows. Medical-grade connectivity ensures devices speak the same language by using established \u003Ca href=\"\u002Fen\u002Fexpertise\u002Fdata-solutions\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"685fea0a-4ce0-4b5d-ba0f-6fb137f51971\" data-entity-substitution=\"canonical\" title=\"EXPERTISE: Data solutions (EN)\">clinical data standards and protocols\u003C\u002Fa>. This interoperability makes it easier for hospitals to adopt your devices and integrate them into their existing environment – reducing friction and improving clinical value.\u003C\u002Fp>",{"infoText":400,"pgSharedParagraphs":401},"6. Scalability and performance considerations",[402],{"id":403,"ctaLink":215,"pgSharedText":404,"pgSharedTitle":400,"pgSharedCtaLink":215},"34507",{"value":405,"processed":405,"format":248},"\u003Cp>Connected medical devices need to perform consistently, whether you manage 100 devices or 1 million. A scalable architecture handles fluctuating data volumes, provides global distribution, and supports edge processing for time-critical tasks. By ensuring predictable performance at every scale, you future-proof your device portfolio and support long-term commercial growth.\u003C\u002Fp>",{"infoText":407,"pgSharedParagraphs":408},"7. Clinical integration and user experience",[409],{"id":410,"ctaLink":215,"pgSharedText":411,"pgSharedTitle":407,"pgSharedCtaLink":215},"34509",{"value":412,"processed":412,"format":248},"\u003Cp>A device’s value is only fully realised when it integrates seamlessly into clinical workflows. Medical-grade connectivity enables intuitive interfaces, decision support tools, and smooth integration with hospital systems. Enabling clinicians to easily access data and insights within their existing workflow improves adoption, leading to better patient outcomes.\u003C\u002Fp>",{"infoText":414,"pgSharedParagraphs":415},"8. Quality assurance and risk management",[416],{"id":417,"ctaLink":215,"pgSharedText":418,"pgSharedTitle":414,"pgSharedCtaLink":215},"34511",{"value":419,"processed":419,"format":248},"\u003Cp>To ensure safe, reliable performance, connected medical devices require rigorous validation. Medical-grade quality assurance includes comprehensive testing environments, automated deployment pipelines, and robust failover strategies. These processes reduce risk, accelerate development, and ensure that updates and new features reach customers safely.\u003C\u002Fp>",{"infoText":421,"pgSharedParagraphs":422},"9. Operational excellence",[423],{"id":424,"ctaLink":215,"pgSharedText":425,"pgSharedTitle":421,"pgSharedCtaLink":215},"34513",{"value":426,"processed":426,"format":248},"\u003Cp>High-performance connected devices generate value long after deployment. Medical-grade operational excellence provides continuous monitoring, audit trails for compliance, predictive maintenance insights, and analytics to improve both technical and clinical outcomes. This holistic approach ensures your connected device ecosystem remains safe, reliable, and optimised over the long term.\u003C\u002Fp>",{"__typename":243,"id":428,"ctaLink":215,"pgSharedText":429,"pgSharedTitle":431,"pgSharedCtaLink":215},"34516",{"value":430,"processed":430,"format":248},"\u003Cp>MedTech is experiencing a watershed moment. AI is redefining what MedTech devices can do. Models are stronger, development cycles are accelerating, regulatory frameworks are maturing, and modern connectivity infrastructure is eliminating the trade-offs that once constrained device intelligence.\u003C\u002Fp>\u003Cp>The gateway to this new era is medical-grade connectivity. It ensures that all data flows are traceable, every update is safe, every insight is actionable, and every device remains compliant across its entire lifecycle. It turns powerful models into powerful products.\u003C\u002Fp>\u003Cp>Build MedTech solutions that scale, remain regulation‑ready, and unlock new clinical and commercial opportunities.\u003C\u002Fp>","Launching medical-grade connectivity for AI in MedTech",{"__typename":433,"id":434,"pgTitle":435,"text":436,"link":437,"coloredBackground":7,"compactMode":7,"actionVariant":26,"buttonText":215,"pgEmbededCode":440},"ParagraphPgCtaDefault","34517","Your journey begins here!","Learn how to achieve medical-grade connectivity and select the best cloud provider for your organisation in our white paper 'Digital transformation in healthcare: the pathway to medical-grade connectivity'.\r\n ",{"title":438,"url":439,"internal":7},"Get the whitepaper","\u002Fen\u002Finsights\u002Fcloud-for-healthcare-medical-grade-connectivity",{"value":441,"processed":441,"format":442},"","code",{"__typename":444,"id":445,"title":446,"lead":447,"collectionLink":215,"coloredBackground":252,"teaserCollection":448},"ParagraphPgTeaserCollection","34659","Learn more about medical-grade connectivity","AI and data-driven technologies are reshaping industries across the board — and healthcare and MedTech are no exception. But unlocking their full potential requires a solid foundation. In this space, the single most critical differentiator between organisations that lead and those that fall behind is medical-grade connectivity.",{"status":7,"title":449,"collectionType":215,"contentType":215,"sharedTaxrefBizImpact":215,"sharedTaxrefFunctions":215,"sharedTaxrefExpertises":215,"sharedTaxrefIndustries":215,"taxrefCareersTopics":215,"contentFormat":215,"flagshipContent":252,"contentOwner":450,"teaser":451},"Medical-grade connectivity Teaser - Blogpost 1","business",[452,480,516],{"__typename":6,"id":453,"path":439,"sharedTeaserText":454,"sharedTeaserTitle":456,"sharedTeaserRefMedia":457,"insightExpertiseTerms":476},"26972",{"processed":455},"\u003Cp>\u003Cspan>Which healthcare cloud platform fits your MedTech strategy? Use our interactive scorecard and explore a nine-pillar evaluation framework.\u003C\u002Fspan>\u003C\u002Fp>","Cloud for healthcare: your blueprint for medical-grade connectivity",{"id":458,"sharedMediaLegend":215,"mediaImage":459,"name":475},"173020",{"url":460,"width":461,"height":462,"alt":463,"title":215,"size":464,"mime":222,"variations":215,"teaserCollectionVariations":465},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FCloud%20for%20healthcare_Header.png",1920,1080,"Healthcare professional handling an IV solution.  ",3354618,[466,469,470,473,474],{"name":225,"url":467,"width":218,"height":468},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FCloud%20for%20healthcare_Header.png.webp?itok=hypardgp",533,{"name":225,"url":467,"width":218,"height":468},{"name":471,"url":472,"width":258,"height":218},"FOCAL_POINT32_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002FCloud%20for%20healthcare_Header.png.webp?itok=fHybOWl1",{"name":471,"url":472,"width":258,"height":218},{"name":471,"url":472,"width":258,"height":218},"Cloud for healthcare_Header.png",[477],{"id":478,"name":479},"124","Cloud",{"__typename":6,"id":481,"path":482,"sharedTeaserText":483,"sharedTeaserTitle":485,"sharedTeaserRefMedia":486,"insightExpertiseTerms":503},"26975","\u002Fen\u002Finsights\u002Fconnected-medical-devices-pilot-to-production",{"processed":484},"\u003Cp>Most connected medical device programmes stall between pilot and production. This 90-day roadmap shows how to break through — from discovery to scale.\u003C\u002Fp>","Medical-grade connectivity: from pilot to production in 90 days",{"id":487,"sharedMediaLegend":215,"mediaImage":488,"name":502},"172996",{"url":489,"width":490,"height":491,"alt":260,"title":215,"size":492,"mime":493,"variations":215,"teaserCollectionVariations":494},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FCharming%20young%20woman%20undergoing%20electroencephalography.jpg",5052,3368,6907589,"image\u002Fjpeg",[495,497,498,500,501],{"name":225,"url":496,"width":218,"height":468},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FCharming%20young%20woman%20undergoing%20electroencephalography.jpg.webp?itok=68bk8f8H",{"name":225,"url":496,"width":218,"height":468},{"name":471,"url":499,"width":258,"height":218},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002FCharming%20young%20woman%20undergoing%20electroencephalography.jpg.webp?itok=oxa2IWDW",{"name":471,"url":499,"width":258,"height":218},{"name":471,"url":499,"width":258,"height":218},"Charming young woman undergoing electroencephalography.jpg",[504,507,510,513],{"id":505,"name":506},"19","Digital strategy",{"id":508,"name":509},"945","Prototyping",{"id":511,"name":512},"20","Hardware engineering",{"id":514,"name":515},"885","IoT & connected devices",{"__typename":6,"id":517,"path":518,"sharedTeaserText":519,"sharedTeaserTitle":521,"sharedTeaserRefMedia":522,"insightExpertiseTerms":539},"26789","\u002Fen\u002Finsights\u002Fwill-the-next-biotech-breakthrough-be-digital-before-its-biological",{"processed":520},"\u003Cp>Read the conversation between David Elliman and Bibi Ephraim, Head of Digital Sciences at Genentech, about how AI is redefining the biotech landscape.\u003C\u002Fp>","Will digital come before biology in biotech’s next leap?",{"id":523,"sharedMediaLegend":215,"mediaImage":524,"name":538},"172502",{"url":525,"width":526,"height":527,"alt":528,"title":215,"size":529,"mime":222,"variations":215,"teaserCollectionVariations":530},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FThumbnail_Episode_4_Bibi_Ephraim.png",1280,720,"A promotional graphic for the podcast “Tech Tomorrow with David Elliman.” On the left, there’s a portrait of a man wearing glasses and a maroon academic-style gown with a white shirt and matching tie. A label below the image reads “Bibi Ephraim.” The background features a dynamic blue and purple gradient design with abstract wave-like patterns.",2052664,[531,533,534,536,537],{"name":225,"url":532,"width":218,"height":468},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FThumbnail_Episode_4_Bibi_Ephraim.png.webp?itok=Hne72IDN",{"name":225,"url":532,"width":218,"height":468},{"name":471,"url":535,"width":462,"height":527},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002FThumbnail_Episode_4_Bibi_Ephraim.png.webp?itok=DH3u-OnL",{"name":471,"url":535,"width":462,"height":527},{"name":471,"url":535,"width":462,"height":527},"Thumbnail_Episode_4_Bibi_Ephraim.png",[540],{"id":541,"name":542},"110","AI",[544,547],{"id":545,"name":546},"826","Healthcare providers",{"id":548,"name":549},"13","MedTech",[551,552,555,558],{"id":541,"name":542},{"id":553,"name":554},"125","Cybersecurity",{"id":556,"name":557},"875","Data",{"id":505,"name":506},{"processed":560},"\u003Cp>\u003Ca href=\"\u002Fen\u002Fexpertise\u002Fai-implementation\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"915b40f2-5188-4476-b86c-7e831565887f\" data-entity-substitution=\"canonical\" title=\"EXPERTISE: AI implementation (EN)\">Artificial intelligence\u003C\u002Fa> is rewriting what \u003Ca href=\"\u002Fen\u002Findustries\u002Fmedtech\" target=\"_blank\" data-entity-type=\"node\" data-entity-uuid=\"fbdb1630-8e7c-4ee2-9034-ed75c9bf2d2b\" data-entity-substitution=\"canonical\" title=\"INDUSTRY: MedTech (EN)\">MedTech\u003C\u002Fa> can achieve, but real advances won’t come from smarter models alone. Today, the difference between promise and impact hinges on a secure, compliant, high‑performance data highway that allows AI to operate safely at scale. The right connectivity enables powerful AI to operate safely, learn continuously, and deliver value in the clinical moments that matter.\u003C\u002Fp>",{"timestamp":562},1774267200,[564],{"status":7,"contactName":565,"contactProfession":566,"contactLinkedinUrl":215,"sharedEmail":215,"sharedRefMedia":567},"Ronnie Bose","Former Group Head Devices",{"id":568,"sharedMediaLegend":215,"mediaImage":569,"name":581},"167210",{"url":570,"width":571,"height":218,"alt":260,"title":215,"size":572,"mime":493,"variations":215,"authorVariations":573},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fronnie_bose_dec2023.jpg",600,170639,[574,577,578,579,580],{"name":575,"url":576,"width":571,"height":571},"FOCAL_POINT11_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_1_1_small\u002Fpublic\u002Fimages\u002Fronnie_bose_dec2023.jpg.webp?itok=7tRw9M_N",{"name":575,"url":576,"width":571,"height":571},{"name":575,"url":576,"width":571,"height":571},{"name":575,"url":576,"width":571,"height":571},{"name":575,"url":576,"width":571,"height":571},"ronnie_bose_dec2023.jpg",["Reactive",451],{"data":584},{"relatedContentView":585},{"id":586,"pageInfo":587,"results":590},"3dad2dac-900c-48b3-9380-0814b1c1f4f1",{"page":588,"pageSize":589,"total":589},0,3,[591,621,664],{"id":592,"path":593,"sharedTeaserTitle":594,"sharedTeaserRefMedia":595,"sharedTaxrefIndustries":612,"sharedTaxrefExpertises":616},"26507","\u002Fen\u002Finsights\u002Fai-adoption-rethinking-time-and-purpose-in-the-workplace","AI adoption: Rethinking time and purpose in the workplace",{"id":596,"sharedMediaLegend":215,"mediaImage":597,"name":611},"171801",{"url":598,"width":599,"height":600,"alt":260,"title":215,"size":601,"mime":222,"variations":215,"relatedContentVariations":602},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fai-adoption.png",754,660,173813,[603,606,607,609,610],{"name":225,"url":604,"width":599,"height":605},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Fai-adoption.png.webp?itok=81HJwjS2",503,{"name":225,"url":604,"width":599,"height":605},{"name":471,"url":608,"width":599,"height":605},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002Fai-adoption.png.webp?itok=327OVyoN",{"name":471,"url":608,"width":599,"height":605},{"name":471,"url":608,"width":599,"height":605},"ai-adoption.png",[613],{"id":614,"name":615},"855","All industries",[617,618],{"id":541,"name":542},{"id":619,"name":620},"1048","Cybernetic Delivery",{"id":622,"path":623,"sharedTeaserTitle":624,"sharedTeaserRefMedia":625,"sharedTaxrefIndustries":641,"sharedTaxrefExpertises":645},"26545","\u002Fen\u002Finsights\u002Fcybernetic-enterprise-industrial-sector","Cybernetic enterprise: Value from AI in the industrial sector",{"id":626,"sharedMediaLegend":215,"mediaImage":627,"name":640},"171898",{"url":628,"width":629,"height":335,"alt":630,"title":215,"size":631,"mime":222,"variations":215,"relatedContentVariations":632},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FWoman%20in%20Cybernetic%20Enterprise.png",1456,"Woman in Cybernetic Enterprise",198318,[633,635,636,638,639],{"name":225,"url":634,"width":218,"height":468},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FWoman%20in%20Cybernetic%20Enterprise.png.webp?itok=F7_Y2d-w",{"name":225,"url":634,"width":218,"height":468},{"name":471,"url":637,"width":258,"height":218},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002FWoman%20in%20Cybernetic%20Enterprise.png.webp?itok=plu-IMQb",{"name":471,"url":637,"width":258,"height":218},{"name":471,"url":637,"width":258,"height":218},"Woman in Cybernetic Enterprise.png",[642],{"id":643,"name":644},"10","Industrial sector",[646,647,648,651,654,655,658,661],{"id":541,"name":542},{"id":619,"name":620},{"id":649,"name":650},"874","GenAI",{"id":652,"name":653},"126","DevOps",{"id":505,"name":506},{"id":656,"name":657},"887","Business transformation",{"id":659,"name":660},"888","Lean & Agile",{"id":662,"name":663},"892","Ecosystems",{"id":665,"path":666,"sharedTeaserTitle":667,"sharedTeaserRefMedia":668,"sharedTaxrefIndustries":685,"sharedTaxrefExpertises":687},"26536","\u002Fen\u002Finsights\u002Fdigital-foundations-why-nhs-transformation-depends-on-interoperability","NHS transformation depends on digital foundations and interoperability",{"id":669,"sharedMediaLegend":670,"mediaImage":671,"name":684},"171891","A female doctor looking outside of a window symbolising the NHS' challenges with it's ongoing transformation. ",{"url":672,"width":526,"height":673,"alt":674,"title":215,"size":675,"mime":493,"variations":215,"relatedContentVariations":676},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FHealing%20Hands%20and%20Healing%20Hearts.jpg",854,"A female doctor looking outside of a window. ",96156,[677,679,680,682,683],{"name":225,"url":678,"width":218,"height":468},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FHealing%20Hands%20and%20Healing%20Hearts.jpg.webp?itok=xEZqSaUX",{"name":225,"url":678,"width":218,"height":468},{"name":471,"url":681,"width":258,"height":218},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002FHealing%20Hands%20and%20Healing%20Hearts.jpg.webp?itok=EaDQtYcI",{"name":471,"url":681,"width":258,"height":218},{"name":471,"url":681,"width":258,"height":218},"Healing Hands and Healing Hearts.jpg",[686],{"id":545,"name":546},[688,689,690,693],{"id":541,"name":542},{"id":556,"name":557},{"id":691,"name":692},"876","Data strategy",{"id":662,"name":663}]