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

Abstract

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.

Keywords: Large models; Medical laboratory data; Obstacles; Opportunities.

Fig. 1

Sources, Features, and Applications of MLD. This figure outlines the different sources and characteristics of MLD, emphasizing their multidimensionality, diverse formats, complexity, dynamics, and time series features. It classifies MLD into clinical tests, laboratory biomolecular omics data and physiological monitoring from portable devices, demonstrating how these data types are used in risk prediction, screening, diagnosis, and treatment strategies in various medical fields.

Fig. 2

Obstacles and solutions to the Medical Laboratory Data Model. This figure outlines the main challenges faced in developing MLD-based models and the corresponding solutions. The obstacles include data quality issues, computational demands, lack of interpretability, and concerns regarding data privacy and security. Solutions include utilizing distributed computing technologies, high-performance hardware, deep learning frameworks, and techniques like federated learning, differential privacy, and homomorphic encryption. Additionally, strategies for improving model optimization, interpretability, and fairness are suggested, such as incorporating regularization layers, residual connections, LASSO regression, and SHAP for interpretability, as well as addressing data and algorithmic biases to enhance model fairness and security.