[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119636-en":3,"doc-seo-119636-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119636,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",7,"Healthcare","Time to reality check the promises of machine learning-powered precision medicine - Viewpoint","Machine learning combined with large electronic health databases promises personalised medicine by improving diagnosis and predicting individual treatment responses. The argument challenges this vision by separating genuine potential from hype and highlighting limitations that algorithmic complexity cannot solve. The discussion evaluates evidence behind automation and clinical usefulness, emphasizes weaknesses in study design and reporting, and calls for collaboration between traditional medical methodologists and machine-learning experts to reduce research waste.","Viewpoint  \nTime to reality check the promises of machine learningpowered precision medicine  \nJack Wilkinson, Kellyn F Arnold, Eleanor J Murray, Maarten van Smeden, Kareem Carr, Rachel Sippy, Marc de Kamps, Andrew Beam, Stefan Konigorski, Christoph Lippert, MarkS Gilthorpe, Peter WG Tennant  \nMachine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.  \nIntroduction  \nProponents of precision medicine make a compelling pitch: traditional approaches to health science have focused too much on comparing effectiveness in the average person and too little on the needs of actual individuals.1,2 The blame could lie with outdated statistical and epidemiological tools, which might offer decreasing relevance to the needs of contemporary clinical decision making.3 The proposed solution speaks to the zeitgeist: our newfound abundance of detailed and accessible longitudinal data on individuals combined with the practical realisation of various flexible machine learning approaches offer an exciting chance for revolution.2 At the apex sits the dream of precision medicine crafted by machine learning, a new framework that promises to revolutionise how we identify the best therapy for each person as an individual, while automating everyday tasks like diagnosis and prognostication with unprecedented accuracy.4  \nBut how realistic are these claims? And when, if ever, can we expect them to be routinely realised? We consider the evidence underlying two of the most common claims about the potential of machine learning powered precision medicine and call for a reality check of expectations.  \nClaim 1: machine learning will enable automated diagnoses with unprecedented accuracy  \nMachine learning is often heralded by health and medical commentators as a powerful prediction tool that will revolutionise disease screening and diagnosis. The inherent flexibility and scope for automation makes machine learning well suited to examining complex high dimensional data (ie, with many variables or features) that would be challenging to model using conventional approaches. Such strategies have enabled the development of several innovative diagnostic algorithms—for example, to identify patients most in need of intervention from knee MRI,5 to detect cardiac arrhythmias from electrocardiograms,6 and to diagnose pneumonia from chest x rays.7  \nGiven such innovation, it is hard to dispute the revolutionary potential of machine learning for improving  \nclinical diagnostics. However, acknowledging potential is a poor substitute for robust scientific evidence of actual benefit, and here research is lacking.8 Although news media is filled with enthusiastic stories about novel machine learning applications,9–12 a systematic review comparing the performance of deep learning versus health professional assessment in diagnosis of various diseases from medical images makes for sobering reading.13 Only 20 (24%) of the 82 studies identified evaluated the performance of their algorithm in an external cohort, and only 14 (17%) studies compared this out of sample performance with that of health professionals. This number is alarmingly small, especially given that many of the studies were flawed. The authors found that reporting standards were typically poor, internal validation was weak and, perhaps most worryingly, model performance was oft","cbCaiiuxdrdAF3px","https://ap.wps.com/l/cbCaiiuxdrdAF3px","pdf",95425,1,4,"English","en",105,"# Introduction\n## Claim 1: Automated diagnoses with unprecedented accuracy\n### Evidence gaps and external validation\n### Clinical utility versus predictive performance\n# Reality check and collaboration call","[{\"question\":\"What central promise does machine learning-powered precision medicine make?\",\"answer\":\"It aims to use large health datasets to improve diagnosis and predict individual responses to therapies, enabling more tailored clinical care. Supporters expect routine accuracy and automation in diagnosis and prognostication.\"},{\"question\":\"Why does the viewpoint caution against accepting machine learning claims at face value?\",\"answer\":\"It stresses that robust scientific evidence of real clinical benefit is limited. Systematic reviews show poor reporting, weak validation, and unrealistic evaluation conditions in many studies.\"},{\"question\":\"What change is proposed to move the field forward?\",\"answer\":\"The authors call for collaboration between traditional methodologists and medical machine-learning experts. This is intended to avoid extensive research waste and improve the rigor of studies.\"}]","Time to reality check the promises of machine learning-powered precision medicine - Viewpoint | PDF",1785725410,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"time-to-reality-check-the-promises-of-machine-learning-powered-precision-medicine-viewpoint","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/time-to-reality-check-the-promises-of-machine-learning-powered-precision-medicine-viewpoint/119636/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What central promise does machine learning-powered precision medicine make?","Question",{"text":74,"@type":75},"It aims to use large health datasets to improve diagnosis and predict individual responses to therapies, enabling more tailored clinical care. Supporters expect routine accuracy and automation in diagnosis and prognostication.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why does the viewpoint caution against accepting machine learning claims at face value?",{"text":79,"@type":75},"It stresses that robust scientific evidence of real clinical benefit is limited. Systematic reviews show poor reporting, weak validation, and unrealistic evaluation conditions in many studies.",{"name":81,"@type":72,"acceptedAnswer":82},"What change is proposed to move the field forward?",{"text":83,"@type":75},"The authors call for collaboration between traditional methodologists and medical machine-learning experts. This is intended to avoid extensive research waste and improve the rigor of studies.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,117,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]