[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123016-en":3,"doc-seo-123016-105":29,"detail-sidebar-cat-0-en-105":91},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123016,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Assessment of the Relevance of Best Practices in the Development of Medical R&D Projects Based on Machine Learning","Machine learning is increasingly used across health informatics, bioinformatics, and medicine, yet many biomedical researchers and IT developers struggle to execute projects correctly due to limited experience. The paper evaluates the importance of best practices for R&D projects that support medical diagnostics. Drawing on literature and authors’ experience, 27 best practices are derived and grouped across three key implementation stages. The study applies the Analytic Hierarchy Process to rank practices using expert, consensus-based judgments, prioritizing DevOps integration, interdisciplinary information sharing, and later automation, with additional emphasis on annotation tools and data/model quality control.","Assessment of the Relevance of Best Practices in the Development of Medical R&D Projects Based on Machine Learning  \nJan Cychnerski  \nDepartment of Computer Architecture, Gda´nsk University of Technology  \nGda´nsk, Poland [jan.cychnerski@pg.edu.pl](jan.cychnerski@pg.edu.pl)  \nTomasz Dziubich  \nDepartment of Computer Architecture, Gda´nsk University of Technology  \nGda´nsk, Poland [tomasz.dziubich@pg.edu.pl](tomasz.dziubich@pg.edu.pl)  \nAbstract  \nMachine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions. The paper presents an assessment of the significance of best practices in the implementation of R&D projects supporting the medical diagnostic process. Based on the literature and authors’ experiences, 27 good practices influencing three fundamental stages of project implementation were identified. The evaluation was based on the Analytic Hierarchy Process, which relies on subjective assessments from experts, whose credibility is expressed through the consensus of assessment. Initially focusing on DevOps methodology, research integration, interdisciplinary information sharing were prioritizedover automation. Furthermore, annotation tools and data / model quality control were identified as of significant importance.  \nKeywords: medical machine learning projects, guidelines, best practices, DevOps, MLOps  \n1. Introduction  \nResearch and Development (R&D) projects represent a distinct category of initiatives, significantly divergent from commercial projects. Their primary aim is the pursuit of innovative solutions, highly consequential for advancing progress within the within the specific research area, thereby fostering economic growth [2] . Consequently, both their motivations, objectives, and execution methods markedly differ from generally construed commercial projects, which primarily focus on application, are market-driven, ensuring revenue for the executing companies, and providing various benefits for potential clients [12] . Unlike commercial projects, R&D projects are divided into two parts: research-oriented, aimed at acquiring new knowledge and creating new solutions, and development-oriented, focusing on building prototypes and laying the groundwork for the potential introduction of a new product to the market. The first stage is characterized by a much higher risk of ultimate failure [24] . This paper focuses on a specific type of R&D IT projects related to supporting medical diagnostics using machine learning methods, defined in this work as the Medical Machine Learning (MedML) project. MedML projects require a complex executive structure due to the interdisciplinary nature of the work and the need for extended data collection, high costs of specialists, interdisciplinary collaboration, research-oriented model building, and client involvement. Ensuring proper organization,  \nWork partially funded by the Cloud Artificial Intelligence Service Engineering (CAISE) project No. KPOD.05.10-IW.10-0005/24, KPO, European Programme IPCEI-CIS  \nadequate financial resources, and effective communication among teams is crucial for success. During the practical implementation and deployment of projects, it is crucial to perform tasks appropriately as this significantly impacts the final outcome of the project. This manner of execution is commonly defined in methodologies through a set of general and detailed recommendations and rules of conduct referred to as best practices or guidelines [18] .  \nThe contributions of this paper are as follows: (1) a comprehensive list of best practices for conducting MedML projects, specifically those that apply machine learning to support m","cbCaibPWQ6b87qiF","https://ap.wps.com/l/cbCaibPWQ6b87qiF","pdf",432010,1,"English","en",105,"# Introduction\n## R&D projects and MedML context\n# Identification of best practices in MedML\n## Need for adaptation and ethical model use","[{\"question\":\"Why do MedML projects need specific best practices compared with other R\\u0026D or commercial projects?\",\"answer\":\"MedML combines interdisciplinary medical diagnostics and machine learning, requiring complex execution structure, extended data collection, high specialist costs, collaboration across fields, and careful deployment. These factors raise the risk of failure and make organization, resources, and communication essential.\"},{\"question\":\"How were the best practices identified in the paper?\",\"answer\":\"The paper uses literature and the authors’ experiences to derive 27 good practices. These practices are organized into three fundamental stages of implementing a medical ML R\\u0026D project.\"},{\"question\":\"What method was used to evaluate the relevance of the best practices?\",\"answer\":\"The evaluation uses the Analytic Hierarchy Process (AHP). AHP relies on subjective expert assessments, and expert credibility is represented through consensus of those assessments.\"}]","Assessment of the Relevance of Best Practices in the Development of Medical R&D Projects Based on Machine Learning | PDF",1785814185,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"assessment-of-the-relevance-of-best-practices-in-the-development-of-medical-rd-projects-based-on-machine-learning","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/assessment-of-the-relevance-of-best-practices-in-the-development-of-medical-rd-projects-based-on-machine-learning/123016/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do MedML projects need specific best practices compared with other R&D or commercial projects?","Question",{"text":75,"@type":76},"MedML combines interdisciplinary medical diagnostics and machine learning, requiring complex execution structure, extended data collection, high specialist costs, collaboration across fields, and careful deployment. These factors raise the risk of failure and make organization, resources, and communication essential.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the best practices identified in the paper?",{"text":80,"@type":76},"The paper uses literature and the authors’ experiences to derive 27 good practices. These practices are organized into three fundamental stages of implementing a medical ML R&D project.",{"name":82,"@type":73,"acceptedAnswer":83},"What method was used to evaluate the relevance of the best practices?",{"text":84,"@type":76},"The evaluation uses the Analytic Hierarchy Process (AHP). 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