[{"data":1,"prerenderedAt":395},["ShallowReactive",2],{"slug-ai-assistance-in-clinical-trials-the-practical-opportunities":3},{"post":4,"relatedPosts":175,"relatedBooks":299},{"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":37,"contact_email_category":39,"yst_prominent_words":40,"class_list":47,"better_featured_image":60,"acf":103,"yoast_meta":120,"_links":122},56453,"2024-06-11T09:30:00","2024-06-11T07:30:00",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=56453&_wpnonce=fb2f5ae10e&status=auto-draft&type=post","2024-06-11T08:32:58","2024-06-11T06:32:58","ai-assistance-in-clinical-trials-the-practical-opportunities","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fai-assistance-in-clinical-trials-the-practical-opportunities",{"rendered":17},"AI Assistance In Clinical Trials: The Practical Opportunities",{"rendered":19,"protected":20},"\n\u003Cp>With overall success rates \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC10173933\u002F\" target=\"_blank\">under 8%\u003C\u002Fa>, conducting clinical trials for drug development is a high-risk endeavour for pharmaceutical and biotechnology companies. However, the process is an essential part of providing and \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC10173933\u002F\" target=\"_blank\">improving healthcare\u003C\u002Fa>, as well as \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fcbeh.centre.uq.edu.au\u002Farticle\u002F2023\u002F06\u002Fwhich-clinical-trials-offer-best-return-on-investment\" target=\"_blank\">a crucial part of the pharmaceutical business\u003C\u002Fa>. Considering the low success rates in this industry, improvements are evidently needed. Recent developments in the artificial intelligence (AI) field indicate that the technology could provide assistance in clinical trials. \u003C\u002Fp>\n\n\n\n\u003Cp>We previously considered the long-term role of AI in clinical trials such as in \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-clinical-trials-artificial-patients-synthetic-data-and-real-time-analysis\u002F\" target=\"_blank\">developing artificial patients\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fin-silico-trials-are-the-future\u002F\" target=\"_blank\">predicting adverse events\u003C\u002Fa>. In this article, we focus on the technology’s near-term, practical potential in assisting in improving the clinical trial process.\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\u002F2019\u002F08\u002F103_In-silico-clinical-trials-768x432.png\" alt=\"in silico trials, synthetic data, artificial patient, TMF\" class=\"wp-image-24545\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F08\u002F103_In-silico-clinical-trials-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F08\u002F103_In-silico-clinical-trials-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F08\u002F103_In-silico-clinical-trials-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F08\u002F103_In-silico-clinical-trials-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F08\u002F103_In-silico-clinical-trials.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Ch2 class=\"wp-block-heading\">Streamlining the clinical trial process from start to finish with AI\u003C\u002Fh2>\n\n\n\n\u003Cp>There are several stages in ensuring safety and efficacy during drug development. By far, the clinical trial stage \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC10173933\u002F\" target=\"_blank\">is considered\u003C\u002Fa> as the most costly and time-consuming one; with costs amounting to $2.6 billion \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC10173933\u002F\" target=\"_blank\">over about a decade\u003C\u002Fa>. There are opportunities for AI assistance during that stage, as well as before it begins and after it ends. We will consider these three categories as potential areas for AI assistance in this article.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Pre-trial AI assistance\u003C\u002Fh2>\n\n\n\n\u003Cp>Before a trial begins, suitable drug candidates need to be identified. This drug discovery process \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww2.deloitte.com\u002Fus\u002Fen\u002Finsights\u002Findustry\u002Flife-sciences\u002Fartificial-intelligence-biopharma-intelligent-drug-discovery.html\" target=\"_blank\">can be accelerated\u003C\u002Fa> with the assistance of AI, thanks to the technology’s ability to analyse vast amounts of data. Pharma company \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.labiotech.eu\u002Fin-depth\u002Fsanofi-ai-deals\u002F\" target=\"_blank\">Sanofi\u003C\u002Fa> has several AI partnerships for such ends, while \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fpharmatimes.com\u002Fnews\u002Fnovo-nordisk-to-open-new-ai-hub-in-uk-for-drug-discovery\u002F\" target=\"_blank\">Novo Nordisk\u003C\u002Fa> plans to open a dedicated AI research branch in London for drug discovery.\u003C\u002Fp>\n\n\n\n\u003Cp>In the case of trials for gene-editing treatments, AI technology in the form of the \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-promise-of-crispr-gpt-specialized-chatgpts-could-transform-medicine\" target=\"_blank\" rel=\"noreferrer noopener\">CRISPR GPT\u003C\u002Fa> can automate their designs. Being specifically trained on gene editing and CRISPR technology datasets, such a tool can understand the intricacies of gene editing, and thereby identify potential errors and suggest optimal experimental designs. Trials for CRISPR therapies \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-state-of-crispr-clinical-trials-and-their-future-potentials\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">are on the rise\u003C\u002Fa>, with the first such treatment having already been approved, and they could benefit from AI assistance. \u003C\u002Fp>\n\n\n\n\u003Cp>After the drug candidate has been identified, an adequate clinical trial execution strategy needs to be devised. One \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7342338\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">significant source of delay\u003C\u002Fa> during this process is the recruitment and retention of participants. With enough quality data used to train an AI model, the technology could assist in designing the trial plan as well as matching and optimising patient populations.\u003C\u002Fp>\n\n\n\n\u003Cp>Once the clinical trial design specifics have been identified, companies can further simulate their trials, before actually running them, with the assistance of AI. This is what Novadiscovery’s in silico trial platform, \u003Ca href=\"https:\u002F\u002Fwww.jinko.ai\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">jinkō\u003C\u002Fa>, does. It could \u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fposts\u002Four-platform-azs-89909469\" target=\"_blank\" rel=\"noreferrer noopener\">predict the real-world results\u003C\u002Fa>, with about 97% accuracy, of an AstraZeneca trial before the company published the results. The in-silico trial took three weeks to design and one hour to execute.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">AI support during clinical trials\u003C\u002Fh2>\n\n\n\n\u003Cp>For clinical trials to begin with human participants, their informed consent is required. Such documents can be drafted by AI tools and reviewed for validity by human experts to streamline the process. Global clinical trial technology company \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.suvoda.com\u002Finsights\u002Fblog\u002Fthe-ai-human-partnership-in-clinical-trials\" target=\"_blank\">Suvoda is working\u003C\u002Fa> on such prototypes with the aim to also improve patient comprehension. \u003C\u002Fp>\n\n\n\n\u003Cp>The technology can also be deployed as a support tool for customers and participants during trials. With a \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002F6-potential-medical-use-cases-for-chatgpt\" target=\"_blank\" rel=\"noreferrer noopener\">generative AI\u003C\u002Fa> trained specifically on relevant datasets, patients can ask specific questions about their consent form while medical professionals can receive summaries of clinical trial documents.\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\u002F2022\u002F12\u002Ftmf_article_347-01-768x432.png\" alt=\"ChatGPT AI algorithm TMF\" class=\"wp-image-48507\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F12\u002Ftmf_article_347-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F12\u002Ftmf_article_347-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F12\u002Ftmf_article_347-01-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F12\u002Ftmf_article_347-01-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F12\u002Ftmf_article_347-01.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>During the trial, AI can also assist in optimising resource management. For example, in multi-site trials, it can forecast demands based on the participant population’s specifics and allocate drugs and other resources accordingly. Pharma giant \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fcognitiveworld\u002F2020\u002F12\u002F26\u002Fthe-increasing-use-of-ai-in-the-pharmaceutical-industry\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">GlaxoSmithKline already uses AI\u003C\u002Fa> to make such predictions so as to inform its customers of distribution and stock.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Post-trial AI assistance\u003C\u002Fh2>\n\n\n\n\u003Cp>Upon completion of a clinical trial, drug companies have to analyse the data output and clinical outcomes. This can represent a significant task for researchers but AI technology can assist in the process. Roche has developed an internal generative AI tool for such assistance. Named \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fdavedrodge_chatgpt-health-rochegpt-activity-7111805972209090561-ATUk\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">RocheGPT\u003C\u002Fa>, the tool can parse through results of clinical trials and scientific publications to produce structured data regarding therapies and patients.\u003C\u002Fp>\n\n\n\n\u003Cp>A similar tool could also provide post-trial support for patients. In the form of a chatbot, it could gather feedback from patients about their experience as well as about their physical and mental health. In case they need assistance, the tool could direct them to the appropriate health services and contacts.\u003C\u002Fp>\n\n\n\n\u003Cp>Whether it is at the start or end of a clinical trial, AI tools may misinterpret nuances or output inaccuracies. This is why it is critical to include human supervision to review AI output in clinical trial assistance. Ultimately, the technology has to augment human capacity, and not replace it. This is more so in the case of clinical trials where the human touch and empathy will remain essential to provide patient care, while AI optimises data-intensive tasks under human supervision.\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>Clinical trials are costly and slow, have low success rates but are essential in improving healthcare. Let’s see the various ways AI can help the process.\u003C\u002Fp>\n",16,56461,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33],7079,504,490,[35,36],7965,566,[38],950,[],[41,42,43,44,45,46],1607,1621,1635,1833,3339,3357,[48,14,49,50,51,52,53,54,55,56,57,58,59],"post-56453","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tmf","category-artificial-intelligence","category-future-of-pharma","tag-ai-in-clinical-trials","tag-clinical-trials","project_category-medical-professionals",{"id":24,"alt_text":27,"caption":27,"description":27,"media_type":61,"media_details":62,"post":5,"source_url":102},"image",{"width":63,"height":64,"file":65,"filesize":66,"sizes":67,"image_meta":99},6667,3750,"2024\u002F06\u002Ftmf_article_418.png",351359,{"medium":68,"large":75,"thumbnail":81,"medium_large":86,"1536x1536":87,"2048x2048":93},{"file":69,"width":70,"height":71,"mime-type":72,"filesize":73,"source_url":74},"tmf_article_418-370x208.png",370,208,"image\u002Fpng",31575,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-370x208.png",{"file":76,"width":77,"height":78,"mime-type":72,"filesize":79,"source_url":80},"tmf_article_418-768x432.png",768,432,92673,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-768x432.png",{"file":82,"width":83,"height":83,"mime-type":72,"filesize":84,"source_url":85},"tmf_article_418-150x150.png",150,13900,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-150x150.png",{"file":76,"width":77,"height":78,"mime-type":72,"filesize":79,"source_url":80},{"file":88,"width":89,"height":90,"mime-type":72,"filesize":91,"source_url":92},"tmf_article_418-1536x864.png",1536,864,178842,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-1536x864.png",{"file":94,"width":95,"height":96,"mime-type":72,"filesize":97,"source_url":98},"tmf_article_418-2048x1152.png",2048,1152,241076,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-2048x1152.png",{"aperture":100,"credit":27,"camera":27,"caption":27,"created_timestamp":100,"copyright":27,"focal_length":100,"iso":100,"shutter_speed":100,"title":27,"orientation":100,"keywords":101},"0",[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418.png",{"cta_type":104,"cta_color":27,"subtitle":27,"key_takeaways":105,"related_books":112,"related_posts_footer":116,"related_posts":20},"subscribe",[106,108,110],{"title":107},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">Clinical trials are costly and time-consuming endeavours with low success rates but are essential in improving healthcare.\u003C\u002Fspan>\u003C\u002Fp>\n",{"title":109},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">Artificial intelligence technology can aid in streamlining the clinical trial process before a trial begins, during a trial as well as after a trial ends.\u003C\u002Fspan>\u003C\u002Fp>\n",{"title":111},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">Adopting such technologies in clinical trials will require human supervision and will not replace the need for the human touch and empathy.\u003C\u002Fspan>\u003C\u002Fp>\n",[113,114,115],55605,47427,34151,[117,118,119],56419,56371,56253,{"yoast_wpseo_title":17,"yoast_wpseo_metadesc":121,"yoast_wpseo_canonical":15},"Clinical trials are costly and slow, have low success rates but are essential in improving healthcare. Let's see the various ways AI can help the process.",{"self":123,"collection":129,"about":132,"author":135,"replies":138,"version-history":141,"predecessor-version":145,"wp:featuredmedia":149,"wp:attachment":152,"wp:term":155,"curies":171},[124],{"href":125,"targetHints":126},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56453",{"allow":127},[128],"GET",[130],{"href":131},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[133],{"href":134},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[136],{"embeddable":26,"href":137},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[139],{"embeddable":26,"href":140},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=56453",[142],{"count":143,"href":144},10,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56453\u002Frevisions",[146],{"id":147,"href":148},56487,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56453\u002Frevisions\u002F56487",[150],{"embeddable":26,"href":151},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F56461",[153],{"href":154},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=56453",[156,159,162,165,168],{"taxonomy":157,"embeddable":26,"href":158},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=56453",{"taxonomy":160,"embeddable":26,"href":161},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=56453",{"taxonomy":163,"embeddable":26,"href":164},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=56453",{"taxonomy":166,"embeddable":26,"href":167},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=56453",{"taxonomy":169,"embeddable":26,"href":170},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=56453",[172],{"name":173,"href":174,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[176],{"id":119,"date":177,"date_gmt":178,"guid":179,"modified":177,"modified_gmt":178,"slug":181,"status":13,"type":14,"link":182,"title":183,"content":185,"excerpt":187,"author":23,"featured_media":189,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":190,"categories":191,"tags":194,"project_category":196,"contact_email_category":197,"yst_prominent_words":198,"class_list":206,"better_featured_image":211,"acf":240,"yoast_meta":255,"_links":257},"2025-06-02T09:59:19","2025-06-02T07:59:19",{"rendered":180},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=56253&_wpnonce=57c5463fcb&status=auto-draft&type=post","cough-monitoring-solutions-the-current-digital-health-landscape","https:\u002F\u002Fmedicalfuturist.com\u002Fcough-monitoring-solutions-the-current-digital-health-landscape",{"rendered":184},"Cough Monitoring Solutions: The Current Digital Health Landscape",{"rendered":186,"protected":20},"\n\u003Cp>Let’s try a quick guessing game (without searching for the answer online): how often does a healthy person cough in a day? If you’ve guessed 4.6 coughs, then you’ve hit the bull’s eye as this is what \u003Ca href=\"https:\u002F\u002Frespiratory-research.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12931-023-02585-1\" target=\"_blank\" rel=\"noreferrer noopener\">the most recent study indicates\u003C\u002Fa>. We usually only pay attention to our coughing if it becomes more frequent and noticeable. In fact, cough accounts for \u003Ca href=\"https:\u002F\u002Fwww.healthcareitnews.com\u002Fnews\u002Fhow-care-cough-one-top-medical-complaints-can-benefit-rpm-and-ai\" target=\"_blank\" rel=\"noreferrer noopener\">one in five reasons\u003C\u002Fa> we seek assistance from a healthcare provider.\u003C\u002Fp>\n\n\n\n\u003Cp>While it is a common symptom of an underlying health condition, ranging from reflux to lung cancer, cough has traditionally not been an easy metric to track. \u003C\u002Fp>\n\n\n\n\u003Cp>We didn’t need technologies to be able to differentiate between patients based on coughs, as primary care physicians have been doing that for centuries. But one’s cough can provide insights for self-monitoring, and digital health advances have started to facilitate the cough-tracking process. These could even make cough monitoring as common as step tracking to inform individual patients and provide doctors with deeper health insights. \u003C\u002Fp>\n\n\n\n\u003Cp>In this article, we will consider the digital health tools that are facilitating cough monitoring. These can essentially be categorized into two groups: smartphone apps and specialized hardware. However, in each group, artificial intelligence (AI) is playing a central role in shaping the cough monitoring landscape to better aid patients in understanding their health.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The power of cough information\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>One question that will inevitably arise regarding cough monitoring is: why do it at all? If we’ve been managing without such a metric so far, then is there any reason to invest further in this? It turns out that there are plenty of reasons to do so as cough is a pretty potent \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fwhat-do-digital-biomarkers-mean\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">biomarker\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cp>Having even basic information about a person’s daily cough count and pattern can assist them in making more informed decisions. For example, those with conditions like COPD and asthma \u003Ca href=\"https:\u002F\u002Fwww.healthcareitnews.com\u002Fnews\u002Fhow-care-cough-one-top-medical-complaints-can-benefit-rpm-and-ai\" target=\"_blank\" rel=\"noreferrer noopener\">could glean insights\u003C\u002Fa> into whether they are about to experience a flare-up. This could prevent exacerbations which would otherwise need intensive and costly treatment. In other cases, an increased incidence of cough in a region could be indicative of allergens or an infection. Public health authorities could use this information to take adequate measures.\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\u002F2022\u002F08\u002Ftmf_article_331-01-768x432.png\" alt=\"AI algorithm research topic digital health\" class=\"wp-image-47103\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F08\u002Ftmf_article_331-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F08\u002Ftmf_article_331-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F08\u002Ftmf_article_331-01-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F08\u002Ftmf_article_331-01-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F08\u002Ftmf_article_331-01.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>“There is a tremendous value in simply empowering patients and doctors with this information,” \u003Ca href=\"https:\u002F\u002Ftime.com\u002F6267654\u002Fai-track-coughing-health\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">says Dr. Peter Small\u003C\u002Fa>, chief medical officer at Hyfe AI, a company that develops cough monitoring tools. “It’s going to transform the whole clinical approach for this common and chronic symptom. Patients will come in, have the data on how much they are coughing, and the physician can suggest a treatment based on that information to see if it makes the coughs better.”\u003C\u002Fp>\n\n\n\n\u003Cp>This future that Dr. Small envisions might not be too far off as there are digital cough monitoring tools that are already available and more that are coming to the market.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Tracking apps for coughs\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>As the average person uses \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ftechreport.com\u002Fstatistics\u002Fmobile-app-download-statistics\u002F\" target=\"_blank\">9 smartphone apps daily\u003C\u002Fa>, having an app for cough monitoring might be the most familiar way to capture this metric. Currently, available apps such as \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.coughtracker.com\u002F\" target=\"_blank\">CoughTracker\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fcoughpro.com\u002F\" target=\"_blank\">CoughPro\u003C\u002Fa> identify and record coughs from the user, analyse them through AI models, and provide contextual summaries. \u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"433\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F05\u002Fcough-tracking-app-768x433.jpg\" alt=\"\" class=\"wp-image-56255\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F05\u002Fcough-tracking-app-768x433.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F05\u002Fcough-tracking-app-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F05\u002Fcough-tracking-app-1536x866.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F05\u002Fcough-tracking-app.jpg 1653w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: https:\u002F\u002Fwww.technologyreview.com\u002F\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>Other similar AI-powered cough-tracking apps are being developed by \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fswaasa.ai\u002F\" target=\"_blank\">Swasaa\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fraisonance.ai\u002Fsolutions.htm\">Raisonance\u003C\u002Fa>. Swasaa’s AI models function by combining cough recordings with other health metrics such as temperature and oxygen saturation to assess a patient’s lung performance. Through such an approach, the company aims to provide an alternative to spirometry, which is more affordable and accessible for pulmonary healthcare. \u003C\u002Fp>\n\n\n\n\u003Cp>In the case of Raisonance, the company began by developing the \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fshowcase\u002Faudiblehealth-dx\u002Fabout\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">AudibleHealth Dx app\u003C\u002Fa> to identify COVID infections from cough recordings. Once a recording is taken, the app connects to the cloud for processing and analysis with the assistance of AI. They are \u003Ca href=\"https:\u002F\u002Fwww.technologyreview.com\u002F2024\u002F01\u002F05\u002F1086196\u002Fai-powered-apps-are-tracking-the-sounds-of-sickness\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">now expanding to\u003C\u002Fa> influenza and tuberculosis diagnosis with the same technology.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Research insights with dedicated, cough-monitoring hardware \u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Aimed primarily for research purposes, dedicated cough-monitoring hardware can provide further insights. Currently, only a few such hardware options are available on the market. One example is the clinically validated \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fvitalograph.com\u002Fclinical-trials\u002Fproduct-solutions\u002Fvitalojak\u002F\" target=\"_blank\">VitaloJAK\u003C\u002Fa>. It functions as a wearable that tracks up to 24 hours of cough recordings. The companion software’s algorithm analyses the data, separates non-cough recordings, and identifies awake and sleep periods. The device has been used in Phase 2 and 3 studies as a cost- and time-effective solution to monitor patients’ cough patterns.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\">\u003Cdiv class=\"wp-block-embed__wrapper\">\n\u003Ciframe loading=\"lazy\" title=\"VitaloJAK LinkedIn\" width=\"640\" height=\"360\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FFTXsb5lbaX4?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen>\u003C\u002Fiframe>\n\u003C\u002Fdiv>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Another hardware option is the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fstradoslabs.com\u002Fcough-monitoring\u002F\" target=\"_blank\">RESP Biosensor\u003C\u002Fa> from Strados, which is also aimed at clinical trials. It is a wearable patch that allows researchers to continuously cough and lung sounds for subsequent analysis. A patient-oriented option in production is the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Frespiri.co\u002Fuk\u002F\" target=\"_blank\">wheezo system from Respiri\u003C\u002Fa>. It detects abnormal breath sounds such as wheeze and allows users to generate logs and potential triggers. The recording can then be played back to a healthcare professional for review. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The AI push in cough monitoring\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Over time, we will likely see more cough-tracking apps and hardware become available on the market. Some might target specific conditions, while others might aim at providing some fitness insights to healthy people, akin to fitness trackers. While they will have differences in their individual aims, cough monitoring tools appear to increasingly have one common feature: AI. \u003C\u002Fp>\n\n\n\n\u003Cp>All of the examples listed above rely on an underlying algorithm that analyses the cough recording sample. Applying machine learning algorithms to such ends is being termed as acoustic AI. \u003C\u002Fp>\n\n\n\n\u003Cp>“When trained on sufficiently large and diverse collections of annotated sounds, these approaches can recognise and classify sounds such as a heart murmur, a wheeze or a cough,” \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.healthcareitnews.com\u002Fnews\u002Fhow-care-cough-one-top-medical-complaints-can-benefit-rpm-and-ai\" target=\"_blank\">explains Hyfe’s Dr. Peter Small\u003C\u002Fa>. “And, when incorporated into digital stethoscopes, they can improve the diagnostic capabilities of clinicians. By tracking their frequency, they can assist with medical management decisions. And ultimately, when incorporated in consumer products, they will empower patients to have a better understanding of and be better able to cope with their chronic medical conditions.” \u003C\u002Fp>\n\n\n\n\u003Cp>In addition to empowering patients with respiratory conditions or simply monitoring their health with a new metric, cough tracking can improve clinical assessments. However, the development of efficient AI tools for such ends will rely on quality training data. Developers of such software will have to address privacy concerns and obtain explicit consent from users to use their cough recordings. \u003C\u002Fp>\n\n\n\n\u003Cp>Already tech giants \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ftime.com\u002F6267654\u002Fai-track-coughing-health\u002F\" target=\"_blank\">like Google\u003C\u002Fa> are showing interest in providing such tools to the general public, which might heighten privacy anxieties. As the field of cough monitoring is relatively nascent and evolving, it would benefit from taking these concerns into account at an early stage. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cem>Written by Dr. Bertalan Meskó & Dr. Pranavsingh Dhunnoo\u003C\u002Fem>\u003C\u002Fp>\n",{"rendered":188,"protected":20},"\u003Cp>Digital health technologies can make cough monitoring as common as sleep tracking – providing brand new insights for health management. 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style=\"font-weight: 400;\">Cough is a common symptom that has traditionally not been easy to track as a health metric.\u003C\u002Fspan>\u003C\u002Fp>\n",{"title":245},"\u003Cp>New digital health technologies, including specialised hardware and artificial intelligence-based software, can provide more details into one’s coughing habit for self-evaluation as well as clinical assessments.\u003C\u002Fp>\n",{"title":247},"\u003Cp>This AI-powered push in cough monitoring promises to empower patients and improve clinical assessments; provided that privacy concerns are addressed during development.\u003C\u002Fp>\n",[113,249,250],37033,30419,[252,253,254],17898,14484,22092,{"yoast_wpseo_title":184,"yoast_wpseo_metadesc":256,"yoast_wpseo_canonical":182},"Digital health technologies can make cough monitoring as common as sleep tracking - providing brand new insights for health 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20 Digital Health Trends For The Near Future",{"rendered":313,"protected":20},"\n\u003Cp>Digital technology could help transform unsustainable healthcare systems, provide cheaper, faster, and more effective solutions for diseases – and could lead to healthier individuals living in healthier communities.\u003C\u002Fp>\n\n\n\n\u003Cp>In this book, we analyze the top 20 trends shaping the future of healthcare, and what they all look like in practice.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003C\u002Fp>\n",{"rendered":315,"protected":20},"\u003Cp>Digital technology could help transform unsustainable healthcare systems, provide cheaper, faster, and more effective solutions for diseases – and could lead to healthier individuals living 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20 Digital Health Trends For The Near Future - The Medical Futurist","Digital Health Trends: In this book, we analyze the top 20 trends shaping the future of healthcare, and what they all look like in 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