[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116891-en":3,"doc-seo-116891-105":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},116891,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning as an enabler of medical technology","Advances in digital computing, data storage, and access to large digitized healthcare datasets are making machine learning central to medical technology. Beyond optimizing complex clinical workflows, it enables scalable image screening, data inference, and automatic diagnostics. The special issue highlights research where machine learning is a core driver of innovation across system planning, radiology, interventional procedures, early disease identification, and rehabilitation.","1 Machine learning as an enabler of medical technology  \n2 Mario Ettore Giardinia, Tinashe Mutsvangwab 3  \n4  \n5 a University of Strathclyde  \n6 Department of Biomedical Engineering  \n7 Wolfson Centre  \n8 106 Rottenrow  \n9 Glasgow G4 0NW  \n10 United Kingdom  \n11 Tel. +44 (0) 141 5483042  \n12 [Email ](Email mario.giardini@strath.ac.uk)[mario.giardini@strath.ac.uk](Email mario.giardini@strath.ac.uk)  \n13  \n14 bTinashe Mutsvangwa  \n15 Division of Biomedical Engineering  \n16 Department of Human Biology  \n17 UCT Faculty of Health Sciences  \n18 Anzio Rd, Observatory, Cape Town, 7935  \n19 South Africa  \n20 Tel. +27 (0)21 650 1418  \n[21](21 Email tinashe.mutsvangwa@uct.ac.za)[ Email ](21 Email tinashe.mutsvangwa@uct.ac.za)[tinashe.mutsvangwa@uct.ac.za](21 Email tinashe.mutsvangwa@uct.ac.za)[ ](21 Email tinashe.mutsvangwa@uct.ac.za)22  \n23  \n24 Driven by advancements in digital computing, data storage, and the availability of large datasets from  \n25 digitized healthcare workflows and telemedicine, machine learning is swiftly becoming integral to the  \n26 most diverse aspects of medical technology. It's not merely about optimizing complex clinical tasks;  \n27 it's also about fostering innovative applications such as large-scale image screening, data inference, 28 and automatic diagnostics. Indeed, machine learning is a prerequisite for a radically new approach to  \n29 these tasks, transcending the re-implementation of established technologies. This special issue  \n30 spotlights papers where machine learning is an essential constituent of medical technology innovation. 31  \n32 At system and planning level, Hajati et al. use machine learning for mental health services analysis, 33 and Jiao et al. enhance radiotherapy plans for nasopharyngeal cancer. Machine learning's role in early  \n34 disease identification is highlighted by Nesaragi et al. for coronary disease, by Din et al. for cerebral  \n35 haemorrhages and by Kuluozturk et al. for diagnosing Covid-19, heart failure, and acute asthma. In  \n36 radiology, Kramer et al. utilize machine learning on femur scans to estimate missing bone geometry  \n37 and Asvadi et al. reconstruct the femur shape from partial data, opening new perspectives for bone  \n38 repair and lower limb therapy and rehabilitation. In interventional procedures, Lamassoure et al.  \n39 showcase a machine learning-assisted instrument for rhinoplasty and Agarwal et al. propose machine  \n40 learning to predict temperature rise during bone drilling. In rehabilitation, Bamdad et al. employ  \n41 machine learning to estimate the knee's mechanical properties, enhancing rehabilitation therapy and  \n42 informing the design of active orthoses.  \n43  \n44 We extend our gratitude to the authors and reviewers for their contributions. We would like to honor  \n45 the late Tania Samantha Douglas, who proposed the original idea of this special issue, and to whom  \n46 the issue is dedicated.","cbCairSI1PhxFLez","https://ap.wps.com/l/cbCairSI1PhxFLez","pdf",191421,1,2,"English","en",105,"# Overview\n## Applications Across Medical Technology\n## Acknowledgements","[{\"question\":\"Why is machine learning becoming central to medical technology?\",\"answer\":\"Because digital computing and data storage advancements, along with large digitized healthcare datasets and telemedicine workflows, make machine learning increasingly integral to diverse medical technology tasks.\"},{\"question\":\"What types of medical technology innovations does the document highlight?\",\"answer\":\"It highlights large-scale image screening, data inference, automatic diagnostics, and improved planning and decision-making across multiple clinical domains.\"},{\"question\":\"How does the special issue organize example research themes?\",\"answer\":\"Examples are grouped by areas such as mental health services analysis, radiotherapy planning, early disease identification, radiology imaging tasks, interventional procedures, and rehabilitation therapy.\"}]","Machine learning as an enabler of medical technology | 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