[{"data":1,"prerenderedAt":399},["ShallowReactive",2],{"slug-the-ai-and-digital-health-future-of-pharma-prescription-for-change":3},{"post":4,"relatedPosts":179,"relatedBooks":311},{"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":41,"contact_email_category":43,"yst_prominent_words":44,"class_list":57,"better_featured_image":74,"acf":107,"yoast_meta":124,"_links":126},56571,"2026-04-30T10:23:18","2026-04-30T08:23:18",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=56571&_wpnonce=6b6a915dc6&status=auto-draft&type=post","2026-04-30T10:23:20","2026-04-30T08:23:20","the-ai-and-digital-health-future-of-pharma-prescription-for-change","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fthe-ai-and-digital-health-future-of-pharma-prescription-for-change",{"rendered":17},"The AI And Digital Health Future Of Pharma: Prescription For Change",{"rendered":19,"protected":20},"\n\u003Cp>Over the past decade, our lead researcher, Dr. Bertalan Meskó, has delivered hundreds of keynote speeches to leading pharmaceutical companies worldwide. As The Medical Futurist, he spends his days mapping out the future of healthcare, a complex and fascinating task. And while his work covers all facets of medicine, from regulation and policies to clinical work, research, and technological development, the pharma sector is probably the one he has spent most of his time on in the past 15 years. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Major current digital health and AI trends in pharma\u003C\u002Fh2>\n\n\n\n\u003Cp>Over the years, he has identified some clear trends that will shape the future of the pharmaceutical landscape. Some of these trends are unmistakably on the horizon, others present a more speculative glimpse into what may come. In this article, we will explore both the obvious and the less certain impacts of the digital health and AI revolution on the pharma industry – as seen in 2024.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Using AI in low-risk, high-ROI fields\u003C\u002Fh3>\n\n\n\n\u003Cp>One of the most promising applications of AI in the pharmaceutical industry is in low-risk, high-return-on-investment (ROI) fields such as:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>drug design, \u003C\u002Fli>\n\n\n\n\u003Cli>drug repurposing, \u003C\u002Fli>\n\n\n\n\u003Cli>and clinical trials. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>In drug design, AI algorithms can analyse vast datasets to identify potential drug candidates more quickly and accurately than traditional methods. \u003C\u002Fp>\n\n\n\n\u003Cp>Bristol Myers Squibb \u003Ca href=\"https:\u002F\u002Fwww.fiercebiotech.com\u002Fbiotech\u002Fbristol-myers-pays-exscientia-20m-1-2b-deal-for-first-drug-candidate\">has partnered with Exscientia\u003C\u002Fa> to use AI for small-molecule drug discovery. The collaboration will use AI to accelerate the discovery of small-molecule therapeutic drug candidates in multiple therapeutic areas, including oncology & immunology. The company also announced \u003Ca href=\"https:\u002F\u002Finvestors.exscientia.ai\u002Fpress-releases\u002Fpress-release-details\u002F2023\u002FExscientia-Announces-Expansion-of-its-Current-Collaboration-with-Sanofi-to-Include-Existing-Exscientia-Programme\u002Fdefault.aspx\">a collaboration with Sanofi\u003C\u002Fa> in 2023. \u003C\u002Fp>\n\n\n\n\u003Cp>Merck forged AI drug-development collaborations with American-Israeli biotech company Biolojic Design and with U.S. company Caris Life Sciences. In September 2023, the company started working with U.K.-based AI specialists BenevolentAI and Exscientia, aiming to significantly reduce drug discovery timelines – \u003Ca href=\"https:\u002F\u002Fwww.wsj.com\u002Ftech\u002Fbiotech\u002Fgermanys-merck-bets-on-ai-drug-design-partnerships-rules-out-acquisitions-interview-943c5b7a\">The Wall Street Journal reported\u003C\u002Fa> in June 2024. \u003C\u002Fp>\n\n\n\n\u003Cp>Clinical trials alone are a vast field for AI, we \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fai-assistance-in-clinical-trials-the-practical-opportunities\">recently analysed this segment\u003C\u002Fa> and listed examples of how various algorithms can be used in pre-trial assistance, how they can support trials, and what they can do after the trials. Tools like \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-promise-of-crispr-gpt-specialized-chatgpts-could-transform-medicine\">CRISPR-GPT\u003C\u002Fa> can help us automate trial designs. In-silico platforms can predict real-world results with high accuracy. Novadiscovery’s jinkō \u003Ca href=\"https:\u002F\u002Fwww.novadiscovery.com\u002Fnovadiscovery-announces-success-of-first-of-its-kind-clinical-trial-simulation-to-accurately-predict-findings-of-phase-iii-clinical-study\u002F#:~:text=The%20jink%C5%8D%2Dpredicted%20findings%20were,a%20median%20TTP%20of%2025.9%20%5B\">predicted the results of an AstraZeneca trial\u003C\u002Fa> with about 97% accuracy before the company published the results. And it took three weeks to design, one hour to execute – and had a cost of a couple thousand dollars.\u003C\u002Fp>\n\n\n\n\u003Cp>Here is an excellent confirmation that \u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS135964462400134X\">AI-generated drugs can indeed perform well\u003C\u002Fa> in clinical trials. “In Phase I, we find AI-discovered molecules have an 80–90% success rate, substantially higher than historic industry averages. In Phase II the success rate is ∼40%, albeit on a limited sample size, comparable to historic industry averages.”\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"799\" height=\"629\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002F1715598207270.jpg\" alt=\"AI drug discovery\" class=\"wp-image-56573\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002F1715598207270.jpg 799w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002F1715598207270-768x605.jpg 768w\" sizes=\"auto, (max-width: 799px) 100vw, 799px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Using generative AI to enhance company processes\u003C\u002Fh3>\n\n\n\n\u003Cp>Pharmaceutical companies are not only using AI to discover new therapies but also to optimize their internal operations. Roche, for instance, has \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fdavedrodge_chatgpt-health-rochegpt-activity-7111805972209090561-ATUk\u002F\">introduced RocheGPT,\u003C\u002Fa> an internal generative AI chatbot designed to streamline repetitive tasks, help intra-team knowledge sharing, and augment analysts’ efforts by analysing scientific articles or clinical test results and then extracting structured data about therapies and patients. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">From patient centricity slowly to patient design\u003C\u002Fh3>\n\n\n\n\u003Cp>The traditional pharma landscape has often operated with a paternalistic “we are making decisions about you” attitude towards patients. However, this dynamic is gradually shifting towards a more collaborative “we are making decisions with you” approach. \u003Ca href=\"https:\u002F\u002Fwww.jmir.org\u002F2022\u002F8\u002Fe39178\">This evolution\u003C\u002Fa> is driven by the growing recognition that patients are not supposed to be just passive recipients of care, but active members of their health team.\u003C\u002Fp>\n\n\n\n\u003Cp>Patient design involves patients as co-creators in the design and development of healthcare solutions. And this is certainly something the vast \u003Ca href=\"https:\u002F\u002Fnewsroom.accenture.com\u002Fnews\u002F2014\u002Fpatients-expect-pharmaceutical-companies-to-provide-services-that-help-them-manage-their-health-accenture-survey-finds\">majority of patients want\u003C\u002Fa>. In the past decade or so, policy makers started adopting this theme too. The US Food and Drug Administration (FDA) launched the \u003Ca href=\"https:\u002F\u002Fwww.fda.gov\u002Fabout-fda\u002Fdivision-patient-centered-development\u002Fcdrh-patient-engagement-advisory-committee\">Patient Engagement Advisory Committee\u003C\u002Fa> in 2017. The committee provides advice to the FDA commissioner or designee on complex issues relating to medical devices, the regulation of devices, and their use by patients.\u003C\u002Fp>\n\n\n\n\u003Cp>This shift is long overdue. After all, patients are the ultimate “customers” of healthcare, and their experiences and needs should be at the forefront of every decision. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Automating the supply chain\u003C\u002Fh3>\n\n\n\n\u003Cp>The pharmaceutical supply chain is undergoing a significant transformation through the integration of \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Frobotics-blockchain-redesign-pharma-supply-chain\u002F\">robotics, AI, and blockchain technology\u003C\u002Fa>. These advancements streamline operations, enhance efficiency, and ensure greater transparency and security. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F05\u002Ftmf_article_270-01-768x432.png\" alt=\"TMF, drug, flu, woman, cold\" class=\"wp-image-34583\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F05\u002Ftmf_article_270-01-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F05\u002Ftmf_article_270-01-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F05\u002Ftmf_article_270-01-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F05\u002Ftmf_article_270-01.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>For example, robotics can automate repetitive tasks such as sorting, packaging, and labeling medications, minimizing the risk of human error and freeing up personnel for more complex tasks. While fake drugs are increasingly becoming a problem all over the world (yes, in developed countries too), technology \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffighting-fake-drugs-with-miniscule-printed-watermarks-a-genius-idea\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">offers ingenious answers\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.ijrte.org\u002Fwp-content\u002Fuploads\u002Fpapers\u002Fv10i1\u002FA57440510121.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Blockchain-based identification of each box\u003C\u002Fa> provides a secure, immutable ledger for tracking the provenance and movement of pharmaceutical products, ensuring traceability from production to patient delivery​. But we’ve seen other creative ideas, such as printing \u003Ca href=\"https:\u002F\u002Fwww.futurity.org\u002Fcounterfeit-medications-fake-drugs-cyberphysical-watermarks-2711322\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">tiny, edible, all-protein unique watermarks\u003C\u002Fa> on individual pills.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Investing in digital therapeutics\u003C\u002Fh3>\n\n\n\n\u003Cp>Pharmaceutical companies are increasingly recognizing the potential of \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-new-buzz-these-are-the-top-examples-of-digital-therapeutics\u002F\">digital therapeutics (DTx)\u003C\u002Fa> as a \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fdrugs-vs-digital-therapeutics-a-symbiotic-rivalry\u002F\">complement to traditional medications\u003C\u002Fa>. DTx are evidence-based software applications designed to prevent, manage, or treat medical conditions. They offer a personalised, scalable, and accessible approach to healthcare, and pharma companies are investing heavily in this emerging field.\u003C\u002Fp>\n\n\n\n\u003Cp>Roche, for instance, was an early adopter by \u003Ca href=\"https:\u002F\u002Fwww.mobihealthnews.com\u002Fcontent\u002Froche-acquires-mysugr-new-core-its-digital-diabetes-management-efforts\">acquiring mySugr\u003C\u002Fa>, a leading diabetes management app, to enhance its portfolio of digital solutions for patients with diabetes. Similarly, \u003Ca href=\"https:\u002F\u002Fwww.mckinsey.com\u002Findustries\u002Flife-sciences\u002Four-insights\u002Fdigital-therapeutics-preparing-for-takeoff\">GSK partnered with Propeller Health\u003C\u002Fa>, a digital therapeutics company specializing in respiratory diseases, to develop and commercialise digital solutions for patients with asthma and COPD. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Launching (and sometimes closing) digital health units\u003C\u002Fh3>\n\n\n\n\u003Cp>“If you’re not in, you’re out” best describes the sentiment most pharma companies have about the digital health revolution. Many market players decided to establish dedicated units focused on developing and commercialising digital solutions. \u003C\u002Fp>\n\n\n\n\u003Cp>Bayer, for instance, \u003Ca href=\"https:\u002F\u002Fwww.bayer.com\u002Fmedia\u002Fen-us\u002Fbayer-launches-unit-to-develop-new-precision-health-consumer-products\u002F\">launched a new unit\u003C\u002Fa> to develop precision health consumer products, while AstraZeneca \u003Ca href=\"https:\u002F\u002Fwww.bloomberg.com\u002Fnews\u002Farticles\u002F2023-11-20\u002Fastrazeneca-starts-health-tech-business-to-add-ai-to-pharma\">started a health tech business\u003C\u002Fa> to integrate AI into its pharmaceutical offerings. \u003C\u002Fp>\n\n\n\n\u003Cp>However, not all ventures are successful, and some companies have even closed down their digital health units. Biogen, for example, \u003Ca href=\"https:\u002F\u002Fpharmaphorum.com\u002Fnews\u002Fbiogen-shuts-digital-health-unit-and-exits-apple-alliance\">recently shut down its digital health unit\u003C\u002Fa> and ended its collaboration with Apple on a digital health app for Parkinson’s disease.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">Let’s see the future! \u003C\u002Fh2>\n\n\n\n\u003Cp>All the trends we’ve discussed so far are already here, happening around us, even if the transformation might be slow or almost invisible in some cases. As progress won’t stop, we can also pinpoint a few advancements that are not yet here, but will surely arrive in the next few years. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">In-silico trials and artificial patients\u003C\u002Fh3>\n\n\n\n\u003Cp>Imagine a world where clinical trials don’t involve recruiting thousands of patients, don’t take years to finish, and don’t cost billions of dollars. This is \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fin-silico-trials-are-the-future\u002F\">the promise of in silico trials\u003C\u002Fa>, where digital representations of human biology are used to test the safety and efficacy of new drugs. While still in its early stages, in silico trials also have the potential to help identify potential safety concerns earlier in the process, potentially sparing patients from unnecessary risks.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-768x432.png\" alt=\"TMF, health data, trial \" class=\"wp-image-24937\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health-512x288.png 512w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002F110_All-the-things-we-can-measure-in-digital-health.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>As we mentioned in the first segment of the article, we see the first studies, but we are not yet capable of actually executing trials this way. Significant challenges remain, including the need for more sophisticated models of human biology and regulatory acceptance. \u003C\u002Fp>\n\n\n\n\u003Cp>Despite these hurdles, in silico trials represent a tantalizing glimpse into the future of drug development, where virtual simulations could complement or even replace traditional clinical trials.\u003C\u002Fp>\n\n\n\n\u003Cp>Another interesting and futuristic concept related to the topic is the idea of \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-future-of-clinical-trials-artificial-patients-synthetic-data-and-real-time-analysis\u002F\">artificial patients\u003C\u002Fa>. (An artificial patient is a set of data representing the desired human characteristics the best possible way that is based on large amounts of real patient data, without actually including any backtracable real-patient data.) \u003C\u002Fp>\n\n\n\n\u003Cp>One day, virtual patients might become the go-to tools for estimating efficiency and potential side effects of \u003Ca href=\"https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC7577280\u002F\">promising drug molecules\u003C\u002Fa> or \u003Ca href=\"https:\u002F\u002Fbiblio.ugent.be\u002Fpublication\u002F8711822\u002Ffile\u002F8711823.pdf\">optimising the use of existing ones\u003C\u002Fa>, to model the success rate of \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002F2077-0375\u002F12\u002F6\u002F548\">future medical devices\u003C\u002Fa> or \u003Ca href=\"https:\u002F\u002Fwww.medgadget.com\u002F2021\u002F06\u002Fin-silico-clinical-trial-replicates-results-of-traditional-trial.html\">treatment methods\u003C\u002Fa>, or, as the latest,\u003Ca href=\"https:\u002F\u002Fwww.theverge.com\u002F2022\u002F4\u002F28\u002F23044586\u002Fvr-chronic-pain-synthetic-clinical-trial-data\"> they can substitute the placebo control group\u003C\u002Fa> for clinical trials. \u003C\u002Fp>\n\n\n\n\u003Cp>As many hope, one day artificial patients may be able to completely substitute humans and animals in clinical trials, most likely with animals being the first. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">Leaving digital transformation behind\u003C\u002Fh3>\n\n\n\n\u003Cp>The phrase “digital transformation” has been a buzzword in the pharmaceutical industry for years. However, as digital technologies become increasingly integrated into every aspect of pharma operations, \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fpharma-companies-and-digital-transformation\u002F\">we may be entering a post-digital transformation era\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cp>In this new era, digital tools and technologies are no longer seen as separate initiatives, but as fundamental components of the pharmaceutical landscape. AI, machine learning, and data analytics will be seamlessly woven into drug discovery, clinical trials, marketing, and patient engagement.\u003C\u002Fp>\n\n\n\n\u003Cp>Pharmaceutical companies that thrive in this post-digital era will be those with a mindset where technology is not just a tool, but a catalyst for innovation and transformation. This will require a fundamental shift in culture, processes, and organisational structures. It will also require a willingness to experiment, take risks, and continuously adapt to the ever-evolving digital landscape.\u003C\u002Fp>\n\n\n\n\u003Cp>In the pharma-future, digital technologies can’t be just add-ons, but the foundation of delivering value to patients.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\">3D Printing Drugs\u003C\u002Fh3>\n\n\n\n\u003Cp>The technology for \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ffuture-3d-printing-drugs-pharmacies-closer-think\u002F\">3D printing drugs is at an exciting stage\u003C\u002Fa> of development, with several approved drugs and ongoing trials. Aprecia Pharmaceuticals’ Spritam, approved by the FDA in 2015, was the first 3D-printed drug designed for epilepsy patients. \u003C\u002Fp>\n\n\n\n\u003Cp>FabRx, a UK-based biotech company, is \u003Ca href=\"https:\u002F\u002F3dprinting.com\u002Fnews\u002Ffirst-3d-printed-pediatric-medicine-trials-to-begin-in-europe\u002F\">conducting the first pediatric clinical trial\u003C\u002Fa> of 3D-printed medicines in Europe, to explore the efficacy and customization of 3D-printed medications in real-world healthcare environments. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1527\" height=\"859\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002F3d_printed_drugs_poster-edited.png\" alt=\"\" class=\"wp-image-36297\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002F3d_printed_drugs_poster-edited.png 1527w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002F3d_printed_drugs_poster-edited-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002F3d_printed_drugs_poster-edited-768x432.png 768w\" sizes=\"auto, (max-width: 1527px) 100vw, 1527px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>While the technology is still nascent, the potential of 3D-printed drugs to transform the pharmaceutical landscape is undeniable. If pharmacies could print customised pills tailored to an individual’s specific needs, taking into account their age, weight, metabolism, and even genetic profile, this could revolutionise medication adherence and efficacy, as patients receive precisely the right dose in a form that’s easiest for them to take.\u003C\u002Fp>\n\n\n\n\u003Cp>Moreover, 3D printing could enable decentralised drug manufacturing, allowing medications to be produced closer to the point of care, potentially reducing costs and improving access in remote or underserved areas. While regulatory and safety hurdles remain, 3Dp-rinted drugs represent a futuristic trend with the potential to make personalised medicine a reality for millions of patients worldwide.\u003C\u002Fp>\n\n\n\n\u003Cp>The Medical Futurist team keeps a close watch on the news of technologies and innovation in the pharma sector, reporting on all significant developments across our social media channels and providing in-depth analysis right here on our website.\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>Let’s see six existing and three future trends that will determining the AI and digital health future of the pharma industry. \u003C\u002Fp>\n",6,33877,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33],7079,504,490,[35,36,37,38,39,40],568,1530,7967,7969,144,356,[42],950,[],[45,46,47,48,49,50,51,52,53,54,55,56],1681,1833,2133,4485,5139,1599,6067,1617,1621,1623,1631,1635,[58,14,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73],"post-56571","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tmf","category-artificial-intelligence","category-future-of-pharma","tag-drug-development","tag-drug-design","tag-ai-in-pharma","tag-drug-repurposing","tag-artificial-intelligence","tag-pharma-2","project_category-medical-professionals",{"id":24,"alt_text":75,"caption":27,"description":27,"media_type":76,"media_details":77,"post":105,"source_url":106},"TMF, drug, pharma","image",{"width":78,"height":79,"file":80,"sizes":81,"image_meta":103},1920,1080,"2021\u002F04\u002Ftmf_article_258-01.png",{"medium":82,"large":88,"thumbnail":93,"medium_large":97,"1536x1536":98},{"file":83,"width":84,"height":85,"mime-type":86,"source_url":87},"tmf_article_258-01-370x208.png","370","208","image\u002Fpng","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_258-01-370x208.png",{"file":89,"width":90,"height":91,"mime-type":86,"source_url":92},"tmf_article_258-01-768x432.png","768","432","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_258-01-768x432.png",{"file":94,"width":95,"height":95,"mime-type":86,"source_url":96},"tmf_article_258-01-150x150.png","150","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_258-01-150x150.png",{"file":89,"width":90,"height":91,"mime-type":86,"source_url":92},{"file":99,"width":100,"height":101,"mime-type":86,"source_url":102},"tmf_article_258-01-1536x864.png","1536","864","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_258-01-1536x864.png",{"aperture":104,"credit":27,"camera":27,"caption":27,"created_timestamp":104,"copyright":27,"focal_length":104,"iso":104,"shutter_speed":104,"title":27,"orientation":104},"0",33827,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_258-01.png",{"cta_type":108,"cta_color":27,"related_posts_footer":109,"related_posts":20,"related_books":113,"subtitle":27,"key_takeaways":117},"subscribe",[110,111,112],56489,56371,56453,[114,115,116],24764,37033,55605,[118,120,122],{"title":119},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">The digital health and AI revolutions are rewriting the rules of pharma.\u003C\u002Fspan>\u003C\u002Fp>\n",{"title":121},"\u003Cp>Some future trends are crystal clear, others remain almost invisible for now.\u003C\u002Fp>\n",{"title":123},"\u003Cp>Success in this evolving landscape will require a willingness to experiment, take risks, and continuously adapt to digital advancements.\u003C\u002Fp>\n",{"yoast_wpseo_title":17,"yoast_wpseo_metadesc":125,"yoast_wpseo_canonical":15},"Let's see six existing and three future trends that will determining the AI and digital health future of the pharma industry.",{"self":127,"collection":133,"about":136,"author":139,"replies":142,"version-history":145,"predecessor-version":149,"wp:featuredmedia":153,"wp:attachment":156,"wp:term":159,"curies":175},[128],{"href":129,"targetHints":130},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56571",{"allow":131},[132],"GET",[134],{"href":135},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[137],{"href":138},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[140],{"embeddable":26,"href":141},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F6",[143],{"embeddable":26,"href":144},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=56571",[146],{"count":147,"href":148},19,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56571\u002Frevisions",[150],{"id":151,"href":152},60693,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56571\u002Frevisions\u002F60693",[154],{"embeddable":26,"href":155},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F33877",[157],{"href":158},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=56571",[160,163,166,169,172],{"taxonomy":161,"embeddable":26,"href":162},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=56571",{"taxonomy":164,"embeddable":26,"href":165},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=56571",{"taxonomy":167,"embeddable":26,"href":168},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=56571",{"taxonomy":170,"embeddable":26,"href":171},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=56571",{"taxonomy":173,"embeddable":26,"href":174},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=56571",[176],{"name":177,"href":178,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[180],{"id":112,"date":181,"date_gmt":182,"guid":183,"modified":185,"modified_gmt":186,"slug":187,"status":13,"type":14,"link":188,"title":189,"content":191,"excerpt":193,"author":195,"featured_media":196,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":197,"categories":198,"tags":199,"project_category":202,"contact_email_category":203,"yst_prominent_words":204,"class_list":208,"better_featured_image":212,"acf":252,"yoast_meta":266,"_links":268},"2024-06-11T09:30:00","2024-06-11T07:30:00",{"rendered":184},"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","https:\u002F\u002Fmedicalfuturist.com\u002Fai-assistance-in-clinical-trials-the-practical-opportunities",{"rendered":190},"AI Assistance In Clinical Trials: The Practical Opportunities",{"rendered":192,"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",{"rendered":194,"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,{"_acf_changed":20,"footnotes":27},[31,32,33],[200,201],7965,566,[42],[],[205,53,56,46,206,207],1607,3339,3357,[209,14,59,60,61,62,63,64,65,66,210,211,73],"post-56453","tag-ai-in-clinical-trials","tag-clinical-trials",{"id":196,"alt_text":27,"caption":27,"description":27,"media_type":76,"media_details":213,"post":112,"source_url":251},{"width":214,"height":215,"file":216,"filesize":217,"sizes":218,"image_meta":249},6667,3750,"2024\u002F06\u002Ftmf_article_418.png",351359,{"medium":219,"large":225,"thumbnail":231,"medium_large":236,"1536x1536":237,"2048x2048":243},{"file":220,"width":221,"height":222,"mime-type":86,"filesize":223,"source_url":224},"tmf_article_418-370x208.png",370,208,31575,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-370x208.png",{"file":226,"width":227,"height":228,"mime-type":86,"filesize":229,"source_url":230},"tmf_article_418-768x432.png",768,432,92673,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-768x432.png",{"file":232,"width":233,"height":233,"mime-type":86,"filesize":234,"source_url":235},"tmf_article_418-150x150.png",150,13900,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-150x150.png",{"file":226,"width":227,"height":228,"mime-type":86,"filesize":229,"source_url":230},{"file":238,"width":239,"height":240,"mime-type":86,"filesize":241,"source_url":242},"tmf_article_418-1536x864.png",1536,864,178842,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-1536x864.png",{"file":244,"width":245,"height":246,"mime-type":86,"filesize":247,"source_url":248},"tmf_article_418-2048x1152.png",2048,1152,241076,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418-2048x1152.png",{"aperture":104,"credit":27,"camera":27,"caption":27,"created_timestamp":104,"copyright":27,"focal_length":104,"iso":104,"shutter_speed":104,"title":27,"orientation":104,"keywords":250},[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F06\u002Ftmf_article_418.png",{"cta_type":108,"cta_color":27,"subtitle":27,"key_takeaways":253,"related_books":260,"related_posts_footer":263,"related_posts":20},[254,256,258],{"title":255},"\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":257},"\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":259},"\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",[116,261,262],47427,34151,[264,111,265],56419,56253,{"yoast_wpseo_title":190,"yoast_wpseo_metadesc":267,"yoast_wpseo_canonical":188},"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":269,"collection":274,"about":276,"author":278,"replies":281,"version-history":284,"predecessor-version":288,"wp:featuredmedia":292,"wp:attachment":295,"wp:term":298,"curies":309},[270],{"href":271,"targetHints":272},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56453",{"allow":273},[132],[275],{"href":135},[277],{"href":138},[279],{"embeddable":26,"href":280},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[282],{"embeddable":26,"href":283},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=56453",[285],{"count":286,"href":287},10,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56453\u002Frevisions",[289],{"id":290,"href":291},56487,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F56453\u002Frevisions\u002F56487",[293],{"embeddable":26,"href":294},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F56461",[296],{"href":297},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=56453",[299,301,303,305,307],{"taxonomy":161,"embeddable":26,"href":300},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=56453",{"taxonomy":164,"embeddable":26,"href":302},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=56453",{"taxonomy":167,"embeddable":26,"href":304},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=56453",{"taxonomy":170,"embeddable":26,"href":306},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=56453",{"taxonomy":173,"embeddable":26,"href":308},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=56453",[310],{"name":177,"href":178,"templated":26},[312],{"id":116,"date":313,"date_gmt":314,"guid":315,"modified":317,"modified_gmt":318,"slug":319,"status":13,"type":320,"link":321,"title":322,"content":324,"excerpt":326,"author":23,"featured_media":328,"comment_status":25,"ping_status":25,"template":27,"yst_prominent_words":329,"class_list":336,"better_featured_image":339,"acf":363,"yoast_meta":368,"_links":371},"2024-04-15T07:55:03","2024-04-15T05:55:03",{"rendered":316},"https:\u002F\u002Fapi.medicalfuturist.com\u002F?post_type=book&p=55605","2024-05-29T23:36:40","2024-05-29T21:36:40","100-questions-and-answers-about-ai-in-healthcare","book","https:\u002F\u002Fapi.medicalfuturist.com\u002Fbooks\u002F100-questions-and-answers-about-ai-in-healthcare\u002F",{"rendered":323},"100 Questions and Answers about AI in Healthcare",{"rendered":325,"protected":20},"\n\u003Cp>Discover artificial intelligence in healthcare in “100 Questions and Answers About Artificial Intelligence in Healthcare.” This guide explores AI’s impact on patient care, diagnosis, treatment, and policy. Through concise answers, backed by research and real-world examples, unlock AI’s potential to revolutionize medicine. With practical insights and relevant links for further exploration, this book equips readers to navigate the AI-driven future of healthcare effortlessly.\u003C\u002Fp>\n",{"rendered":327,"protected":20},"\u003Cp>Discover artificial intelligence in healthcare in “100 Questions and Answers About Artificial Intelligence in Healthcare.” This guide explores AI’s impact on patient care, diagnosis, treatment, […]\u003C\u002Fp>\n",55611,[330,331,332,333,334,335],1801,1715,1809,1683,1805,1813,[337,320,338,60,62,63],"post-55605","type-book",{"id":328,"alt_text":27,"caption":27,"description":27,"media_type":76,"media_details":340,"post":116,"source_url":362},{"width":341,"height":342,"file":343,"filesize":344,"sizes":345,"image_meta":360},923,1196,"2024\u002F04\u002F100-questions.png",346216,{"medium":346,"large":350,"thumbnail":355,"medium_large":359},{"file":347,"width":221,"height":222,"mime-type":86,"filesize":348,"source_url":349},"100-questions-370x208.png",114709,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002F100-questions-370x208.png",{"file":351,"width":227,"height":352,"mime-type":86,"filesize":353,"source_url":354},"100-questions-768x995.png",995,894359,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002F100-questions-768x995.png",{"file":356,"width":233,"height":233,"mime-type":86,"filesize":357,"source_url":358},"100-questions-150x150.png",36705,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002F100-questions-150x150.png",{"file":351,"width":227,"height":352,"mime-type":86,"filesize":353,"source_url":354},{"aperture":104,"credit":27,"camera":27,"caption":27,"created_timestamp":104,"copyright":27,"focal_length":104,"iso":104,"shutter_speed":104,"title":27,"orientation":104,"keywords":361},[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002F100-questions.png",{"buy_button_text":364,"leanpub_url":365,"preview":366},"Get it on Leanpub","https:\u002F\u002Fleanpub.com\u002F100-questions-about-ai-in-healthcare",[367],{"image":362},{"yoast_wpseo_title":369,"yoast_wpseo_metadesc":370,"yoast_wpseo_canonical":321},"AI in Healthcare - 100 Questions and Answers - The Medical Futurist","Artificial Intelligence in Healthcare - 100 Questions and Answers: Explore AI's impact on healthcare in \"100 Questions & Answers About AI in Healthcare\"",{"self":372,"collection":377,"about":380,"author":383,"replies":385,"wp:featuredmedia":388,"wp:attachment":391,"wp:term":394,"curies":397},[373],{"href":374,"targetHints":375},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fbook\u002F55605",{"allow":376},[132],[378],{"href":379},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fbook",[381],{"href":382},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fbook",[384],{"embeddable":26,"href":141},[386],{"embeddable":26,"href":387},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=55605",[389],{"embeddable":26,"href":390},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F55611",[392],{"href":393},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=55605",[395],{"taxonomy":173,"embeddable":26,"href":396},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=55605",[398],{"name":177,"href":178,"templated":26},1785956509412]