[{"data":1,"prerenderedAt":404},["ShallowReactive",2],{"slug-what-if-generative-ai-turned-to-be-a-flop-in-healthcare":3},{"post":4,"relatedPosts":181,"relatedBooks":305},{"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":33,"project_category":37,"contact_email_category":42,"yst_prominent_words":43,"class_list":50,"better_featured_image":66,"acf":109,"yoast_meta":126,"_links":128},55673,"2026-05-11T10:34:09","2026-05-11T08:34:09",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=55673&_wpnonce=75c5bf66e8&status=auto-draft&type=post","2026-05-11T10:34:10","2026-05-11T08:34:10","what-if-generative-ai-turned-to-be-a-flop-in-healthcare","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fwhat-if-generative-ai-turned-to-be-a-flop-in-healthcare",{"rendered":17},"What If Generative AI Turned To Be A Flop In Healthcare?",{"rendered":19,"protected":20},"\n\u003Cp>The excitement surrounding generative AI is reaching a fever pitch. From tech giants to healthcare leaders, \u003Ca href=\"https:\u002F\u002Fwww.cbinsights.com\u002Fresearch\u002Fgenerative-ai-funding-top-startups-investors-2023\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">investment \u003C\u002Fa>in this seemingly game-changing technology is exploding. We’re embracing the trend: we’ve written \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fsearch\u002F?term=AI\" target=\"_blank\" rel=\"noreferrer noopener\">dozens of articles\u003C\u002Fa>, created multiple videos, published \u003Ca href=\"https:\u002F\u002Fleanpub.com\u002Fgenerative-ai-in-healthcare\" target=\"_blank\" rel=\"noreferrer noopener\">an ebook\u003C\u002Fa>, and recently launched a \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.thinkific.com\u002Fcourses\u002Fshort-guide-to-generative-ai-in-healthcare\" target=\"_blank\" rel=\"noreferrer noopener\">new short course\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>However, amidst the enthusiasm, AI expert Gary Marcus \u003Ca href=\"https:\u002F\u002Fcacm.acm.org\u002Fblogcacm\u002Fwhat-if-generative-ai-turned-out-to-be-a-dud\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">raised an important question\u003C\u002Fa> a few months ago: What if, for all its promise, generative AI fails to deliver long-term? While he outlined the pessimistic scenario in general, I wanted to dissect what genAI being a flop would mean in healthcare and medicine.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">How to define generative AI failure in healthcare?\u003C\u002Fh2>\n\n\n\n\u003Cp>Since the public launch of ChatGPT, we’ve \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fsearch\u002F?term=chatgpt\" target=\"_blank\" rel=\"noreferrer noopener\">explored the potential of generative AI\u003C\u002Fa> in medicine. This technology offers \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fusing-chatgpt-offline-the-emergence-of-small-language-models\" target=\"_blank\" rel=\"noreferrer noopener\">promising applications\u003C\u002Fa>, from enhancing administrative efficiency to functioning \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-evolution-of-clinical-documentation-from-paper-to-ai\" target=\"_blank\" rel=\"noreferrer noopener\">as a virtual medical scribe\u003C\u002Fa>, potentially reshaping how medical facilities operate and interact with patients.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg decoding=\"async\" src=\"https:\u002F\u002Flh7-us.googleusercontent.com\u002FJVJmk_7AuL0GceA0LMbj64Ly95EUeuLUzI-_Emsny7HFIULvqP5-4y-7yAMNg0GpxinGdBsiIeR2VsrOBJ_D2s8v3YZcHlBAgGsA_k_USM1LvFBRiFL71hVcKr45oMtCR1A3SGljTPhWjF4K5C-iVN0\" alt=\"\"\u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>Let’s now consider the other side of the coin. If generative AI fails to live up to the expectations, the consequences for healthcare could be significant. Let’s break down what failure could look like.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>We don’t find evidence that it works\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>A fundamental failure of generative AI would be its inability to be incorporated into evidence-based medicine. Without robust empirical support from well-conducted research and clinical trials, AI technologies can’t be applied in medical practice. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>No clinical trials prove its safety\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Similarly, a major red flag will be if years go by and we don’t see solid clinical trials to evaluate the potential of generative AI.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>And\u002For find proof that it’s useless\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Another clear indicator would be if trials, pilots, and studies would prove that using generative AI in healthcare is inefficient and\u002For unsafe. This could mean AI systems making inaccurate predictions, leading to incorrect treatments, or compromising patient privacy and safety, ultimately causing more harm than good.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Deep fakes rule the information highways\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>The use of AI to create deep fakes could provoke significant ethical concerns and public outrage. This could include being used to falsify medical records, create misleading patient data, deepfake medical authorities \u003Ca href=\"https:\u002F\u002Findianexpress.com\u002Farticle\u002Fcities\u002Fdelhi\u002Fvideo-medanta-hospital-chief-weight-loss-deepfake-gurgaon-police-9223387\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">advocating bogus treatments\u003C\u002Fa> or arguing against clinically proven ones, or fabricating medical advice. These could be life-threatening scenarios, and right now we are not exactly sure how to ensure such content can’t reach the general population. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg decoding=\"async\" src=\"https:\u002F\u002Flh7-us.googleusercontent.com\u002FkUdEtFuynGoJgscKhz11Ulrg4iM_Alg-FgThq4WWbcHkpmKRKXmzm0DOuBYzBma76UNRXiFcYGpaJvGTRAcDQ-Sft9u2D433L5eurYla-yjpYPCVhKLxmLqCz3Ilfl7x_QNtz_tS_pBvCgFvbeXz6Pc\" alt=\"\"\u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003Cstrong>People recognize AI text and don’t find it credible enough\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>As the novelty of generative AI may wane, AI-generated materials – brochures, summaries, etc. – become easily distinguishable and may be seen as less credible and unreliable. Thus generative AI as a tool for creating legitimate medical content could diminish.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">What happens next?\u003C\u002Fh2>\n\n\n\n\u003Cp>Continuing from the potential pitfalls of generative AI in healthcare, let’s explore the broader implications should these technologies fail to fulfill their promises. The consequences would ripple across the healthcare industry affecting public trust, \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fthe-current-state-of-fda-approved-ai-based-medical-devices\" target=\"_blank\" rel=\"noreferrer noopener\">regulatory landscapes\u003C\u002Fa>, and investment dynamics.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Erosion of trust\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Generative AI’s failure to deliver on its promises could erode public trust in AI applications in general, casting doubt on its reliability and effectiveness. This could also slow the adoption of other AI-powered tools in healthcare and beyond.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Leading to bans\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>If generative AI is unsafe or ineffective in healthcare, regulatory bodies might impose restrictions or bans on its use in sensitive environments such as medical schools and hospitals. Such prohibitions would be a protective measure to prevent harm and preserve the integrity of medical education and patient care, but they would also hinder development and limit the technology’s potential benefits.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image\">\u003Cimg decoding=\"async\" src=\"https:\u002F\u002Flh7-us.googleusercontent.com\u002FFGTbpW58nRxxDh5KoRGRQMt_fVJvMsrfCng14uVSIw-eUifGhOaADJjJdqtIQ0rf3D4EkEY_Cwi9GoHhpd2QKJh_6ZTudAlIX6KyZfs7euQOn01VUZY8GIHVpfEj-wi9JAJH2lbuF0181Y73cYKtb-I\" alt=\"\"\u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003Cstrong>Overly stringent regulations\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>In response to potential risks, policymakers might introduce overly stringent regulations, stifling innovation and halting the development of generative AI in healthcare. This could create a bureaucratic quagmire, slowing progress and discouraging investment.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Investors turn away from the field\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>If generative AI fails to demonstrate clear value, investors might lose confidence and pull back their funding. This could lead to a decline in research and development, further delaying or even halting progress in this field.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">However, I don’t think it is a flop\u003C\u002Fh2>\n\n\n\n\u003Cp>Having said all that, I still don’t believe generative AI will be a flop in medicine (or elsewhere). It is different from previous technologies in a crucial way: we don’t need to believe how it works to a handful of experts working in specialised labs. Quite the contrary, we can directly interact with and test it, experiment, and discover its potential. Still, thinking about “what if” questions is rarely a waste of time, as it helps us prepare for the future.\u003C\u002Fp>\n\n\n\n\u003Cp>I think the very nature of generative AI – its accessibility and the ability for daily hands-on use – suggests that it is unlikely to fail outright. Instead, my greater concern lies with its potentially rapid, unregulated development, which could lead to unforeseen consequences and challenges. \u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>What if generative AI fails to live up to the expectations? The consequences for healthcare could be significant. Let’s analyze the worst-case scenario!\u003C\u002Fp>\n",6,55731,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32],7079,504,[34,35,36],144,7691,7933,[38,39,40,41],949,950,952,953,[],[44,45,46,47,48,49],1621,1683,1723,1831,1833,1835,[51,14,52,53,54,55,56,57,58,59,60,61,62,63,64,65],"post-55673","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tmf","category-artificial-intelligence","tag-artificial-intelligence","tag-ai-in-heaalthcare","tag-generative-ai","project_category-educators","project_category-medical-professionals","project_category-policy-makers","project_category-researchers",{"id":24,"alt_text":27,"caption":27,"description":27,"media_type":67,"media_details":68,"post":5,"source_url":108},"image",{"width":69,"height":70,"file":71,"filesize":72,"sizes":73,"image_meta":105},6667,3750,"2024\u002F04\u002Ftmf_article_410.png",1977566,{"medium":74,"large":81,"thumbnail":87,"medium_large":92,"1536x1536":93,"2048x2048":99},{"file":75,"width":76,"height":77,"mime-type":78,"filesize":79,"source_url":80},"tmf_article_410-370x208.png",370,208,"image\u002Fpng",44354,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002Ftmf_article_410-370x208.png",{"file":82,"width":83,"height":84,"mime-type":78,"filesize":85,"source_url":86},"tmf_article_410-768x432.png",768,432,119375,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002Ftmf_article_410-768x432.png",{"file":88,"width":89,"height":89,"mime-type":78,"filesize":90,"source_url":91},"tmf_article_410-150x150.png",150,20270,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002Ftmf_article_410-150x150.png",{"file":82,"width":83,"height":84,"mime-type":78,"filesize":85,"source_url":86},{"file":94,"width":95,"height":96,"mime-type":78,"filesize":97,"source_url":98},"tmf_article_410-1536x864.png",1536,864,293632,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002Ftmf_article_410-1536x864.png",{"file":100,"width":101,"height":102,"mime-type":78,"filesize":103,"source_url":104},"tmf_article_410-2048x1152.png",2048,1152,425355,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002Ftmf_article_410-2048x1152.png",{"aperture":106,"credit":27,"camera":27,"caption":27,"created_timestamp":106,"copyright":27,"focal_length":106,"iso":106,"shutter_speed":106,"title":27,"orientation":106,"keywords":107},"0",[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F04\u002Ftmf_article_410.png",{"cta_type":110,"cta_color":27,"related_books":111,"related_posts_footer":115,"related_posts":20,"subtitle":27,"key_takeaways":119},"subscribe",[112,113,114],52203,55605,24759,[116,117,118],55573,55379,55115,[120,122,124],{"title":121},"\u003Cp>If generative AI fails to demonstrate effectiveness and safety through clinical trials and empirical evidence, it risks exclusion from evidence-based medical practice\u003C\u002Fp>\n",{"title":123},"\u003Cp>A failure of generative AI could erode public trust, provoke ethical concerns with deepfakes, and lead to stringent regulations or outright bans in medical settings, potentially stifling further innovation and investment in the field.\u003C\u002Fp>\n",{"title":125},"\u003Cp>Although I predict the success of generative AI due to its accessibility and usability, analyzing “what if” scenarios is always important as it helps to prepare for future challenges.\u003C\u002Fp>\n",{"yoast_wpseo_title":17,"yoast_wpseo_metadesc":127,"yoast_wpseo_canonical":15},"What if generative AI fails to live up to the expectations? The consequences for healthcare could be significant. Let's analyze the worst-case scenario!",{"self":129,"collection":135,"about":138,"author":141,"replies":144,"version-history":147,"predecessor-version":151,"wp:featuredmedia":155,"wp:attachment":158,"wp:term":161,"curies":177},[130],{"href":131,"targetHints":132},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F55673",{"allow":133},[134],"GET",[136],{"href":137},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts",[139],{"href":140},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fpost",[142],{"embeddable":26,"href":143},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F6",[145],{"embeddable":26,"href":146},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=55673",[148],{"count":149,"href":150},7,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F55673\u002Frevisions",[152],{"id":153,"href":154},55749,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F55673\u002Frevisions\u002F55749",[156],{"embeddable":26,"href":157},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F55731",[159],{"href":160},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=55673",[162,165,168,171,174],{"taxonomy":163,"embeddable":26,"href":164},"category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=55673",{"taxonomy":166,"embeddable":26,"href":167},"post_tag","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=55673",{"taxonomy":169,"embeddable":26,"href":170},"project_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=55673",{"taxonomy":172,"embeddable":26,"href":173},"contact_email_category","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=55673",{"taxonomy":175,"embeddable":26,"href":176},"yst_prominent_words","https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=55673",[178],{"name":179,"href":180,"templated":26},"wp","https:\u002F\u002Fapi.w.org\u002F{rel}",[182],{"id":118,"date":183,"date_gmt":184,"guid":185,"modified":187,"modified_gmt":188,"slug":189,"status":13,"type":14,"link":190,"title":191,"content":193,"excerpt":195,"author":197,"featured_media":198,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":199,"categories":200,"tags":201,"project_category":205,"contact_email_category":206,"yst_prominent_words":207,"class_list":211,"better_featured_image":216,"acf":245,"yoast_meta":260,"_links":262},"2026-03-02T09:06:27","2026-03-02T08:06:27",{"rendered":186},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=55115&_wpnonce=45ed6c25a3&status=auto-draft&type=post","2026-03-02T09:06:28","2026-03-02T08:06:28","using-chatgpt-offline-the-emergence-of-small-language-models","https:\u002F\u002Fmedicalfuturist.com\u002Fusing-chatgpt-offline-the-emergence-of-small-language-models",{"rendered":192},"Using ChatGPT Offline: How Small Language Models Can Aid Healthcare Professionals",{"rendered":194,"protected":20},"\n\u003Cp>By now, you might have come across the term large language models (LLMs), which is \u003Ca href=\"https:\u002F\u002Fblog.google\u002Finside-google\u002Fgooglers\u002Fask-a-techspert\u002Fwhat-is-generative-ai\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">a type of generative artificial intelligence\u003C\u002Fa> (GenAI). If not, you have likely encountered \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fgenerative-ai-explained-its-impact-and-future-in-healthcare\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">GenAI applications\u003C\u002Fa> that are based on LLMs. This includes the likes of ChatGPT, Google Bard and Microsoft Copilot. While such models have proved useful, in the healthcare setting, they come with \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fwhy-and-how-to-regulate-chatgpt-like-large-language-models-in-healthcare\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">new sets\u003C\u002Fa> of regulatory, ethical and privacy concerns.\u003C\u002Fp>\n\n\n\n\u003Cp>Recently, another type of language model has been \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fnews.microsoft.com\u002Fthree-big-ai-trends-to-watch-in-2024\u002F\" target=\"_blank\">gaining attention\u003C\u002Fa> in the GenAI field: the small language model (SLM). It even holds the promise of addressing some of the challenges with integrating LLMs in healthcare. In this article, we will introduce SLMs, compare them to LLMs and explore their healthcare potential.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>What are SLMs?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Like LLMs, SLMs are a type of GenAI and \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fsmall-language-models-what-why-matter-vishnuvaradhan-v-0qypc\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">operate in similar ways\u003C\u002Fa>. This means that they rely on neural networks to learn patterns from language in text to produce new text of their own. \u003C\u002Fp>\n\n\n\n\u003Cp>SLMs are termed as “small” as they are trained \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fsmall-language-models-what-why-matter-vishnuvaradhan-v-0qypc\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">on relatively small\u003C\u002Fa> amounts of data and have a relatively small number of parameters. Parameters here \u003Ca href=\"https:\u002F\u002Fblog.wordbot.io\u002Fai-artificial-intelligence\u002Fnlp-models-the-importance-of-parameters\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">refer to\u003C\u002Fa> variables that define the model’s structure and behavior.\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\u002F2023\u002F07\u002Ftmf_article_380-768x432.png\" alt=\"\" class=\"wp-image-51765\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F07\u002Ftmf_article_380-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F07\u002Ftmf_article_380-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F07\u002Ftmf_article_380-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F07\u002Ftmf_article_380.png 1980w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>Emphasis should be paid on \u003Cstrong>\u003Cem>relatively\u003C\u002Fem> \u003C\u002Fstrong>as SLMs still involve \u003Ca href=\"https:\u002F\u002Fwww.theregister.com\u002F2023\u002F11\u002F13\u002Fgenerative_ai_its_not_just\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">millions or even billions of parameters\u003C\u002Fa>. However, this range of parameters in SLM is “small” in comparison to LLMs and we’ll consider the differences between these models in the next section.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>SLM and LLM: what’s the difference?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>While there is \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fsmall-language-models-what-why-matter-vishnuvaradhan-v-0qypc\u002F\" target=\"_blank\">no clear threshold\u003C\u002Fa> for SLMs, they usually \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.theregister.com\u002F2023\u002F11\u002F13\u002Fgenerative_ai_its_not_just\u002F\" target=\"_blank\">tend to have between\u003C\u002Fa> a hundred million to tens of billions of parameters. While still large numbers, this range is small compared to the number of parameters LLMs possess which can reach hundreds of billions. Consider OpenAI’s GPT-3: this LLM \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fsmall-language-models-what-why-matter-vishnuvaradhan-v-0qypc\u002F\" target=\"_blank\">has 175 billion parameters\u003C\u002Fa>, and GPT-4 is \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.theinformation.com\u002Farticles\u002Fthe-rise-of-small-language-models-and-reinforcement-learning\" target=\"_blank\">believed to have\u003C\u002Fa> about a trillion parameters. In comparison, Microsoft \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fblog\u002Fphi-2-the-surprising-power-of-small-language-models\u002F\" target=\"_blank\">recently introduced Phi-2\u003C\u002Fa>, an SLM developed by the company’s researchers with \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fblog\u002Fphi-2-the-surprising-power-of-small-language-models\u002F\" target=\"_blank\">2.7 billion parameters\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cp>This difference in architecture is reflected in the resources required to run the different types of models. LLMs require \u003Ca href=\"https:\u002F\u002Fventurebeat.com\u002Fai\u002Fgenerative-ai-and-the-big-buzz-about-small-language-models\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">significant computing resources\u003C\u002Fa> from servers to storage. Such needs trickle down to the huge costs associated with running such models; and are thus not accessible or even feasible to every organisation. In comparison, SLMs can be small enough to run \u003Ca href=\"https:\u002F\u002Fnews.microsoft.com\u002Fthree-big-ai-trends-to-watch-in-2024\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">offline on a phone\u003C\u002Fa> while bearing significantly less operational costs.\u003C\u002Fp>\n\n\n\n\u003Cp>“Small language models can make AI more accessible due to their size and affordability,” \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fnews.microsoft.com\u002Fthree-big-ai-trends-to-watch-in-2024\u002F\" target=\"_blank\">says Sebastien Bubeck\u003C\u002Fa>, who leads the Machine Learning Foundations group at Microsoft Research. “At the same time, we’re discovering new ways to make them as powerful as large language models.”\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"439\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ffile-VKCtW6J0oTWs3Xy8XRa19htk-768x439.jpg\" alt=\"\" class=\"wp-image-55157\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ffile-VKCtW6J0oTWs3Xy8XRa19htk-768x439.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ffile-VKCtW6J0oTWs3Xy8XRa19htk-1536x878.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ffile-VKCtW6J0oTWs3Xy8XRa19htk.jpg 1792w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\n\n\n\u003Cp>While a complex model with more parameters can be more powerful, SLMs can still have an edge over LLMs. By being trained on smaller and more specialized datasets, SLMs \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fsmall-language-models-what-why-matter-vishnuvaradhan-v-0qypc\u002F\" target=\"_blank\">can be more efficient\u003C\u002Fa> for specific cases, even if this means having a narrower scope than LLMs.\u003C\u002Fp>\n\n\n\n\u003Cp>This can even lead to SLMs to outperform LLMs in certain cases. Microsoft exemplified this with their Phi-2 SLM, which \u003Ca href=\"http:\u002F\u002Fphi-2\">performed better\u003C\u002Fa> in coding and maths tasks compared to the Llama-2 LLM which is 25 times larger than Phi-2.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>SLMs’ potentials in healthcare\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>By focusing on curated, high-quality data and requiring less computational and financial resources, SLMs are particularly apt for healthcare uses. While GenAI which is based on SLMs has not been publicly released, we can contemplate some of the technology’s potential.\u003C\u002Fp>\n\n\n\n\u003Ch4 class=\"wp-block-heading\">1. Personalised patient journey\u003C\u002Fh4>\n\n\n\n\u003Cp>By training an SLM-based GenAI on relatively small but high-quality datasets, patients can receive a personalized healthcare experience. This can be achieved by developing a chatbot that focuses on a specific condition and can provide patients with educational materials and recommendations specific to their conditions. With such a tool, each patient could even have a personal, artificial doctor’s assistant that guides them during their patient journey.\u003C\u002Fp>\n\n\n\n\u003Ch4 class=\"wp-block-heading\">2. Affordable generative AI\u003C\u002Fh4>\n\n\n\n\u003Cp>By requiring fewer resources to train and run, SLMs are more affordable than their LLM counterparts. Such models could be deployed without the need for costly infrastructure such as specialised hardware and cloud services. Through such increased accessibility, more healthcare institutions could benefit from GenAI and further tune the technology to their individual needs, without compromising on efficiency. \u003C\u002Fp>\n\n\n\n\u003Ch4 class=\"wp-block-heading\">3. Improved AI transparency\u003C\u002Fh4>\n\n\n\n\u003Cp>Thanks to their simpler architecture, SLM outputs are more interpretable and thus more transparent. Transparency over such AI models is further enhanced by the ability to better control the training data to address biases and be more reflective of the population it is assisting. This can further help in building trust in such AI tools.\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\u002F2023\u002F05\u002Ftmf_article_363-768x432.png\" alt=\"\" class=\"wp-image-51097\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F05\u002Ftmf_article_363-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F05\u002Ftmf_article_363-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F05\u002Ftmf_article_363-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F05\u002Ftmf_article_363.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>While SLM tools have yet to be publicly deployed in healthcare settings, their advantages indicate that it is only a matter of time until this happens. Big tech companies are actively working on such models. Microsoft researchers have \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fnews.microsoft.com\u002Fthree-big-ai-trends-to-watch-in-2024\u002F\" target=\"_blank\">developed and released two SLMs\u003C\u002Fa>, namely Phi and Orca. French AI startup Mistral has \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.theinformation.com\u002Farticles\u002Fthe-rise-of-small-language-models-and-reinforcement-learning\" target=\"_blank\">released Mixtral-8x7B\u003C\u002Fa>, which can run on a single computer (with plenty of RAM). Google has \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ftechcrunch.com\u002F2023\u002F12\u002F06\u002Fgoogles-gemini-isnt-the-generative-ai-model-we-expected\u002F\" target=\"_blank\">Gemini Nano\u003C\u002Fa>, which can run on smartphones.\u003C\u002Fp>\n\n\n\n\u003Cp>However, SLM tools will also bring about their respective concerns when they eventually roll out for healthcare purposes. They will have to adhere to similar \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fwhy-and-how-to-regulate-chatgpt-like-large-language-models-in-healthcare\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">regulations we propose for LLMs\u003C\u002Fa> in order to ensure their safe and ethical applications. As \u003Ca href=\"https:\u002F\u002Fnews.microsoft.com\u002Fthree-big-ai-trends-to-watch-in-2024\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft lists\u003C\u002Fa> SLMs as one of the big AI trends to watch in 2024, it might be worthwhile for the healthcare community to do the same.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cem>Written by Dr. Bertalan Meskó & Dr. Pranavsingh Dhunnoo\u003C\u002Fem>\u003C\u002Fp>\n",{"rendered":196,"protected":20},"\u003Cp>SLMs have relatively small number of parameters, and can, for example, run on an average mobile phone, without internet access.\u003C\u002Fp>\n",16,55149,{"_acf_changed":20,"footnotes":27},[31],[202,203,204],7741,7915,7917,[39],[],[45,48,208,209,210],2313,2693,4633,[212,14,52,53,54,55,56,57,213,214,215,63],"post-55115","tag-large-language-models","tag-small-language-models","tag-slm",{"id":198,"alt_text":27,"caption":27,"description":27,"media_type":67,"media_details":217,"post":118,"source_url":244},{"width":69,"height":70,"file":218,"filesize":219,"sizes":220,"image_meta":242},"2024\u002F03\u002Ftmf_article_404.png",1477675,{"medium":221,"large":225,"thumbnail":229,"medium_large":233,"1536x1536":234,"2048x2048":238},{"file":222,"width":76,"height":77,"mime-type":78,"filesize":223,"source_url":224},"tmf_article_404-370x208.png",34214,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_404-370x208.png",{"file":226,"width":83,"height":84,"mime-type":78,"filesize":227,"source_url":228},"tmf_article_404-768x432.png",89420,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_404-768x432.png",{"file":230,"width":89,"height":89,"mime-type":78,"filesize":231,"source_url":232},"tmf_article_404-150x150.png",16072,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_404-150x150.png",{"file":226,"width":83,"height":84,"mime-type":78,"filesize":227,"source_url":228},{"file":235,"width":95,"height":96,"mime-type":78,"filesize":236,"source_url":237},"tmf_article_404-1536x864.png",221872,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_404-1536x864.png",{"file":239,"width":101,"height":102,"mime-type":78,"filesize":240,"source_url":241},"tmf_article_404-2048x1152.png",334238,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_404-2048x1152.png",{"aperture":106,"credit":27,"camera":27,"caption":27,"created_timestamp":106,"copyright":27,"focal_length":106,"iso":106,"shutter_speed":106,"title":27,"orientation":106,"keywords":243},[],"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2024\u002F03\u002Ftmf_article_404.png",{"cta_type":110,"cta_color":27,"subtitle":27,"key_takeaways":246,"related_books":253,"related_posts_footer":256,"related_posts":20},[247,249,251],{"title":248},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">Small language models (SLMs) are gaining attention in the generative artificial intelligence field. SLMs are trained on relatively small amounts of data and have a relatively small number of parameters.\u003C\u002Fspan>\u003C\u002Fp>\n",{"title":250},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">SLM can be more practical than large language models as they are less resource intensive, without compromising on efficiency for specific use cases. \u003C\u002Fspan>\u003C\u002Fp>\n",{"title":252},"\u003Cp>\u003Cspan style=\"font-weight: 400;\">Potentials of SLM in healthcare include personalised patient journeys, making generative AI accessible to more institutions, and improving transparency over AI models.\u003C\u002Fspan>\u003C\u002Fp>\n",[112,254,255],24761,47427,[257,258,259],55033,54593,54819,{"yoast_wpseo_title":192,"yoast_wpseo_metadesc":261,"yoast_wpseo_canonical":190},"SLMs have relatively small number of parameters, and can, for example, run on an average mobile phone, without internet access.",{"self":263,"collection":268,"about":270,"author":272,"replies":275,"version-history":278,"predecessor-version":282,"wp:featuredmedia":286,"wp:attachment":289,"wp:term":292,"curies":303},[264],{"href":265,"targetHints":266},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F55115",{"allow":267},[134],[269],{"href":137},[271],{"href":140},[273],{"embeddable":26,"href":274},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F16",[276],{"embeddable":26,"href":277},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=55115",[279],{"count":280,"href":281},15,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F55115\u002Frevisions",[283],{"id":284,"href":285},55163,"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F55115\u002Frevisions\u002F55163",[287],{"embeddable":26,"href":288},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F55149",[290],{"href":291},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=55115",[293,295,297,299,301],{"taxonomy":163,"embeddable":26,"href":294},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=55115",{"taxonomy":166,"embeddable":26,"href":296},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftags?post=55115",{"taxonomy":169,"embeddable":26,"href":298},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fproject_category?post=55115",{"taxonomy":172,"embeddable":26,"href":300},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcontact_email_category?post=55115",{"taxonomy":175,"embeddable":26,"href":302},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=55115",[304],{"name":179,"href":180,"templated":26},[306],{"id":114,"date":307,"date_gmt":308,"guid":309,"modified":311,"modified_gmt":312,"slug":313,"status":13,"type":314,"link":315,"title":316,"content":318,"excerpt":320,"author":322,"featured_media":323,"comment_status":25,"ping_status":25,"template":27,"yst_prominent_words":324,"class_list":326,"better_featured_image":329,"acf":349,"yoast_meta":373,"_links":375},"2019-09-06T21:56:27","2019-09-06T19:56:27",{"rendered":310},"https:\u002F\u002Fapi.medicalfuturist.com\u002F?post_type=book&p=24759","2023-03-12T18:06:35","2023-03-12T17:06:35","the-technological-future-of-medical-specialties","book","https:\u002F\u002Fapi.medicalfuturist.com\u002Fbooks\u002Fthe-technological-future-of-medical-specialties\u002F",{"rendered":317},"The Technological Future of Medical Specialties",{"rendered":319,"protected":20},"\n\u003Cp>In this e-book we specify in detail how the appearance of artificial intelligence, sensors, wearables, VR\u002FAR or robots affect each medical specialty in order to be able to discern what skills physicians will need in the future. We aim to show how physicians of the various professions can successfully prepare for the sweeping changes coming with the waves of technology. \u003C\u002Fp>\n",{"rendered":321,"protected":20},"\u003Cp>In this e-book we specify in detail how the appearance of artificial intelligence, sensors, wearables, VR\u002FAR or robots affect each medical specialty in order to […]\u003C\u002Fp>\n",10,49971,[325],2015,[327,314,328,53,55,56],"post-24759","type-book",{"id":323,"alt_text":27,"caption":27,"description":27,"media_type":67,"media_details":330,"post":114,"source_url":348},{"width":331,"height":332,"file":333,"filesize":334,"sizes":335,"image_meta":347},320,414,"2019\u002F09\u002Ffuture-of-medical-specialties.png",24785,{"medium":336,"thumbnail":342},{"file":337,"width":338,"height":339,"mime-type":78,"filesize":340,"source_url":341},"future-of-medical-specialties-320x208.png","320","208","52681","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002Ffuture-of-medical-specialties-320x208.png",{"file":343,"width":344,"height":344,"mime-type":78,"filesize":345,"source_url":346},"future-of-medical-specialties-150x150.png","150","26567","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002Ffuture-of-medical-specialties-150x150.png",{"aperture":106,"credit":27,"camera":27,"caption":27,"created_timestamp":106,"copyright":27,"focal_length":106,"iso":106,"shutter_speed":106,"title":27,"orientation":106},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002Ffuture-of-medical-specialties.png",{"leanpub_url":350,"buy_button_text":351,"preview":352},"https:\u002F\u002Fleanpub.com\u002Ffuture-of-medical-specialties","Get it on Leanpub",[353,355,357,359,361,363,365,367,369,371],{"image":354},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-01.png",{"image":356},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-02.png",{"image":358},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-03.png",{"image":360},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-04.png",{"image":362},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-05.png",{"image":364},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-06.png",{"image":366},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-07.png",{"image":368},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-08.png",{"image":370},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-09.png",{"image":372},"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2019\u002F09\u002FThe-Technological-Future-of-Medical-Specialties-2021-Preview-10.png",{"yoast_wpseo_title":374,"yoast_wpseo_metadesc":27,"yoast_wpseo_canonical":315},"The Technological Future of Medical Specialties - The Medical Futurist",{"self":376,"collection":381,"about":384,"author":387,"replies":390,"wp:featuredmedia":393,"wp:attachment":396,"wp:term":399,"curies":402},[377],{"href":378,"targetHints":379},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fbook\u002F24759",{"allow":380},[134],[382],{"href":383},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fbook",[385],{"href":386},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Ftypes\u002Fbook",[388],{"embeddable":26,"href":389},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fusers\u002F10",[391],{"embeddable":26,"href":392},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=24759",[394],{"embeddable":26,"href":395},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F49971",[397],{"href":398},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=24759",[400],{"taxonomy":175,"embeddable":26,"href":401},"https:\u002F\u002Fapi.medicalfuturist.com\u002Fwp-json\u002Fwp\u002Fv2\u002Fyst_prominent_words?post=24759",[403],{"name":179,"href":180,"templated":26},1785589553402]