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<p>Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. This study thoroughly examines the opportunities, challenges, and solutions in this field. The use of large-scale MLD improves diagnostic accuracy and allows for real-time disease monitoring. Additionally, integrating social and environmental data enables the analysis of disease mechanisms and trends. Despite these benefits, challenges such as data quality, model optimization, computational requirements, and limited interpretability remain, along with concerns about data privacy, fairness, and security. Proposed solutions include establishing standardized data formats, utilizing deep learning frameworks, employing distributed computing, improving interpretability, and implementing techniques like federated learning and algorithm optimization to address bias and safeguard privacy. Future directions will focus on enhancing performance in specific scenarios, expanding applications across different domains, increasing transparency, enabling real-time processing, and building a supportive ecosystem. It is essential to strengthen policy oversight and promote collaboration among governments, medical institutions, and academia to ensure that technological advancements align with societal progress.</p>

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Medical laboratory data-based models: opportunities, obstacles, and solutions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月24日

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  • Jiaojiao Meng,
  • Moxin Wu,
  • Fangmin Shi,
  • Ying Xie,
  • Hui Wang,
  • You Guo

摘要

Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. This study thoroughly examines the opportunities, challenges, and solutions in this field. The use of large-scale MLD improves diagnostic accuracy and allows for real-time disease monitoring. Additionally, integrating social and environmental data enables the analysis of disease mechanisms and trends. Despite these benefits, challenges such as data quality, model optimization, computational requirements, and limited interpretability remain, along with concerns about data privacy, fairness, and security. Proposed solutions include establishing standardized data formats, utilizing deep learning frameworks, employing distributed computing, improving interpretability, and implementing techniques like federated learning and algorithm optimization to address bias and safeguard privacy. Future directions will focus on enhancing performance in specific scenarios, expanding applications across different domains, increasing transparency, enabling real-time processing, and building a supportive ecosystem. It is essential to strengthen policy oversight and promote collaboration among governments, medical institutions, and academia to ensure that technological advancements align with societal progress.