Smart Laboratory Diagnostics is an international, peer-reviewed journal dedicated to advancing intelligent, data-driven innovation and transformation of laboratory diagnostics in clinical practice and public health. Spanning the pre-analytical, analytical, and post-analytical phases, SLD embraces significant advances in laboratory diagnostics enabled by artificial intelligence, machine learning, data science, automation, and innovative diagnostic technologies, with particular emphasis on methodological rigor, real-world applicability, and clinical impact.
SLD provides an interdisciplinary platform for laboratory professionals, clinicians, biomedical engineers, data scientists, and healthcare researchers to accelerate innovation and clinical translation in laboratory diagnostics, improving diagnostic accuracy, efficiency, and patient care.
The journal welcomes original research articles, reviews, brief reports, perspectives, and other scholarly contributions relevant to the field of intelligent laboratory diagnostics.
Topics include but are not limited to:
1.Intelligent Laboratory Automation
This section focuses on automation and artificial intelligence that optimize the entire laboratory workflow, from test ordering and specimen collection to analytical processes, result verification, and report generation. It covers total laboratory automation, automated workflow systems, machine-learning based error detection, auto-verification, AI-assisted interpretation, and laboratory robotics that improve laboratory efficiency, accuracy, and patient safety.
2.Intelligent Laboratory Operations and Quality Management
This section focuses on intelligent and data-driven approaches to laboratory operations and quality management. It includes patient-based real-time quality control, performance monitoring and quality improvement using laboratory quality indicators, workload forecasting and resource management, external quality assessment and accreditation, AI-assisted competency assessment, and AI-assisted internal audits.
3.Laboratory Data Mining and Clinical Decision Support
This section focuses on mining laboratory and clinical data to support precision diagnostics and clinical or public health decision-making. It covers reference interval establishment and validation, disease screening, risk prediction, prognostic modeling. It also welcomes real-world evidence from large-scale laboratory datasets, multi-center and multi-omics cohort studies, laboratory data-driven epidemiology and public health surveillance, and AI applications in specific experimental diagnosis domains such as anemia and coagulation, infectious diseases and sepsis, tumor markers and minimal residual disease, molecular and genetic diagnosis, critical care, and therapeutic drug monitoring. Research on multi-modal data integration is encouraged.
4.Artificial Intelligence in Morphological Image Analysis
This section focuses on AI-assisted image analysis of laboratory morphology, including hematology, cytology, histopathology, and microbiology. It covers digital microscopy, digital morphology in hematology, digital cytology, digital pathology, as well as quantitative image analysis and methods for morphological segmentation, recognition, and classification.
5.Algorithms and Methodological Innovations in Laboratory Diagnostics
This section covers the development and evaluation of AI and machine learning algorithms, statistical and data science methods, computational frameworks, reproducible software tools and analytical workflows, and open-source platforms for laboratory diagnostics. It also includes algorithm benchmarking, comparative evaluation, and standardized frameworks for performance assessment.
6.Laboratory Informatics and Data Governance
This section focuses on the generation, capture, standardization, exchange, integration, governance, and reuse of laboratory diagnostic data. It covers laboratory data standardization, interoperability, terminology harmonization, and data quality that support integrated laboratory systems, data sharing, and intelligent diagnostics.
7.Advanced Laboratory Diagnostic Technologies
This section covers novel and emerging technologies for laboratory diagnostics, including point-of-care testing, microfluidic and lab-on-a-chip platforms, high-throughput platforms, biosensors, wearable devices, and single-cell technologies. Research combining these technologies with artificial intelligence and data-driven approaches for clinical applications is particularly encouraged.
8.Biomarker Discovery and Validation
This section focuses on the discovery, development and validation of clinically relevant biomarkers and biomarker panels using bioinformatics, multi-omics integration and other data-driven approaches, including AI and machine learning. Studies should include experimental validation and evaluation in appropriate clinical cohorts.
9.Trustworthy AI in Laboratory Diagnostics
This section covers the validation and responsible implementation of artificial intelligence systems in laboratory diagnostics, with emphasis on robustness, calibration, uncertainty estimation, explainability, fairness, transparency, safety, human oversight, regulatory considerations, and post-deployment monitoring.
Biomarker discovery studies without experimental and clinical validation, and generic algorithmic studies without a substantive laboratory diagnostic application, are outside the journal’s scope. Studies involving human subject data or clinical deployment of AI must comply with applicable ethical, data protection, and regulatory requirements, which should be declared upon submission.
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