[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127169-en":3,"doc-seo-127169-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127169,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning in point-of-care testing - innovations, challenges, and opportunities","Diagnostic testing is transforming as artificial intelligence and machine learning are integrated into decentralized, rapid, and accessible point-of-care testing platforms. The COVID-19 pandemic accelerated the move beyond centralized labs and enabled next-generation POCT that improve accuracy, sensitivity, and operational efficiency. The perspective examines how ML is embedded across lateral and vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, and evaluates key barriers including regulatory constraints, reliability, and privacy for clinical adoption.","UCLA  \nUCLA Previously Published Works  \nTitle  \nMachine learning in point-of-care testing: innovations, challenges, and opportunities.  \nPermalink  \n[https://escholarship.org/uc/item/3233883x](https://escholarship.org/uc/item/3233883x)  \nJournal  \nNature Communications, 16(1)  \nAuthors  \nHan, Gyeo-Re  \nGoncharov, Artem Eryilmaz, Merveet al.  \nPublication Date  \n2025-04-02  \nDOI  \n10.1038/s41467-025-58527-6  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPerspective [https://doi.org/10.1038/s41467-025-58527-6](https://doi.org/10.1038/s41467-025-58527-6)  \nMachine learning in point-of-care testing: innovations, challenges, and opportunities  \nReceived: 17 December 2024  \n\n| Accepted: 25 March 2025 |\n| --- |\n|  |\n| Check for updates |\n\nGyeo-Re Han 1,10, Artem Goncharov1,10, Merve Eryilmaz 1,2, Shun Ye 2,3, Barath Palanisamy 2,3, Rajesh Ghosh 2,3, Fabio Lisi 4, Elliott Rogers 5,  \nDavid Guzman 5, Defne Yigci6, Savas Tasoglu 6,7,8, Dino Di Carlo 2,3, Keisuke Goda 4, Rachel A. McKendry 5 & Aydogan Ozcan 1,2,3,9   \nThe landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-ofcare testing (POCT). The COVID-19 pandemic has accelerated the shift from centralized laboratory testing but also catalyzed the development of nextgeneration POCT platforms that leverage ML to enhance the accuracy, sensitivity, and overall efficiency of point-of-care sensors. This Perspective exploreshow ML is being embedded into various POCT modalities, including lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imagingbased sensors, illustrating their impact through different applications. We also discuss several challenges, such as regulatory hurdles, reliability, and privacy concerns, that must be overcome for the widespread adoption of MLenhanced POCT in clinical settings and provide a comprehensive overview of the current state of ML-driven POCT technologies, highlighting their potential impact in the future of healthcare.  \nThe landscape of diagnostic testing is undergoing a significant transformation, shifting from traditional centralized laboratory testing to more decentralized, rapid, and accessible methods through point-ofcare testing (POCT)1,2. Historically, centralized lab testing has played a crucial role in diagnosing and managing diseases by analyzing biological samples3. However, this often faces challenges related to lengthy turnaround times, high operational costs, and limited accessibility. The COVID-19 pandemic highlighted some of these limitations, as the surge in testing demand exceeded the capacity of centralized labs4. During the pandemic, at-home antigen tests4 and point-of-care nucleic acid testing5 became widespread for testing large populations effectively, demonstrating the feasibility and accuracy of POCT outside traditional lab environments. The widespread implementation of this  \ntesting paradigm has revolutionized diagnostics, providing timely and accessible solutions essential for effective disease management and rapid medical response in diverse healthcare settings.  \nCurrent trends in POCT are guided by the updated REASSURED criteria—Real-time connectivity, Ease of specimen collection, Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipmentfree, and Deliverable to end-users—which set the standard for modern POCT devices6–8. Despite recent advancements, several significant challenges persist across various POCT modalities, including paperbased sensors such as lateralflow assays (LFAs)and vertical flow assays (VFAs), nucleic acid amplification tests (NAATs), and imaging-based sensor technologies4,9–13. Achieving high analytical sensitivity and precision, detecting low-abundance biomarkers in biological samples,  \n1Electrical & Computer Engine","cbCaiqlVq8B8WGKj","https://ap.wps.com/l/cbCaiqlVq8B8WGKj","pdf",3951728,1,34,"English","en",105,"# Introduction\n## Shift from centralized testing to POCT\n# ML-Driven POCT Modalities\n## Lateral flow and vertical flow assays\n## Nucleic acid amplification tests\n## Imaging-based sensors\n# Challenges for Adoption\n## Regulatory hurdles, reliability, and privacy\n## Scalability and user interpretation","[{\"question\":\"What role does machine learning play in point-of-care testing?\",\"answer\":\"Machine learning can be embedded into POCT sensors to improve diagnostic accuracy, sensitivity, and overall efficiency while enabling interpretation of complex multivariable patterns.\"},{\"question\":\"Which POCT modalities are discussed for ML integration?\",\"answer\":\"The document covers lateral flow assays, vertical flow assays, nucleic acid amplification tests, and imaging-based sensors, showing how ML supports different sensing and readout approaches.\"},{\"question\":\"What challenges must be overcome for widespread ML-enhanced POCT adoption?\",\"answer\":\"Key barriers include regulatory hurdles, reliability concerns, and privacy issues, along with the need for robust real-time data processing and error detection.\"}]","Machine learning in point-of-care testing - 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