Overview

Aims and Scope

iLABMED is a high-quality, peer-reviewed, open access journal covering key topics in all aspects of laboratory medicine and related multidisciplinary research, focusing on the computer and data science enabled intelligent medicine, basic science，clinical research，and translational application in laboratory medicine and other emerging interdisciplinary, strategic frontiers and key common technologies in laboratory medicine and related multidisciplinary. The journal aims to provide a platform for the researchers, scholars, administrators, and innovators to share their research achievements in the basic and applied research and frontier clinical laboratory medicine on a scientific basis. The journal is committed to publishing the highest-quality and up-to-date research, promoting the development of health prevention and diagnosis and treatment research. 

Academic Sections

1. Bioinformatics

This section covers advanced computational methods for analyzing biomedical big data. It includes AI algorithms for genomic sequencing, protein structure prediction, and drug discovery. Special focus is given to machine learning applications in precision medicine.

2. Cell Biology

This section includes research on intelligent imaging systems for cellular analysis. It covers AI-powered tracking of cell dynamics, automated microscopy, and computational modeling of cellular processes. Applications in disease mechanism studies are particularly emphasized.

3. Clinical Microbiology

This section focuses on AI-enhanced diagnostic platforms for pathogen identification. It includes studies on antimicrobial resistance prediction using machine learning and automated culture analysis systems. Research on smart infection control strategies is also featured.

4. Clinical Biochemistry

This section covers intelligent systems for biomarker discovery and validation. It includes automated metabolic profiling technologies and AI-assisted interpretation of biochemical test results. Applications in early disease detection and monitoring are highlighted.

5. Clinical Hematology (Body Fluids)

This section includes smart diagnostic systems for blood and body fluid analysis. It covers automated cell counting, AI-powered morphological analysis, and intelligent hematological disorder detection. Research on quality control algorithms is also incorporated.

6. Clinical Immunology

This section focuses on computational approaches to immune system analysis. It includes AI models for vaccine development, immunotherapy optimization, and autoimmune disease prediction. Studies on intelligent immune monitoring systems are particularly welcome.

7. Endocrinology

This section covers intelligent monitoring systems for endocrine disorders. It includes AI-assisted interpretation of hormonal tests and predictive models for metabolic diseases.

8. Intelligent Medicine

This section includes cutting-edge research on medical AI applications. It covers clinical decision support systems, automated diagnosis algorithms, and intelligent treatment planning tools. Studies on ethical and regulatory aspects of medical AI are also considered.

9. Molecular Pathology

This section focuses on AI-powered pathological diagnosis systems. It includes digital pathology solutions, computational histopathology, and intelligent tumor grading algorithms. Research on integrating multi-omics data for precision diagnosis is encouraged.

10. Oncological Biotherapy

This section covers computational approaches to cancer treatment. It includes AI models for predicting immunotherapy responses and optimizing combination therapies. Studies on intelligent systems for personalized treatment planning are prioritized.

11. Preventive Medicine

This section includes predictive modeling for public health applications. It covers AI-based risk assessment tools, intelligent screening systems, and automated health monitoring solutions. Research on implementing these technologies in community settings is featured.

12. Transfusion Science

This section focuses on intelligent systems for blood banking. It includes machine learning algorithms for blood matching, automated quality control systems, and predictive models for transfusion outcomes. Research on improving transfusion safety through AI is highlighted.

13. Neuroscience

This section covers advanced neuroinformatics and brain-computer interfaces. It includes AI applications in neurological disorder diagnosis, intelligent neuroimaging analysis, and computational models of brain function. Studies on implementing these technologies in clinical practice are emphasized.

14. Molecular Genetics

This section includes AI-driven analysis of genetic data for medical applications. It covers genomic variant interpretation, disease risk prediction models, and intelligent systems for genetic counseling. Research on implementing precision medicine through computational genetics is prioritized.

Readership

  • Technicians, physicians and clinicians in laboratory medicine and pathology
  • Specialized persons in basic medicine, translational medicine, and clinic medicine
  • Specialized persons in intelligent medicine

Why publish in iLABMED?

  • iLABMED focuses on basic and applied research and cutting-edge clinical laboratory medicine. You can access to the latest developments in the clinical laboratory sciences.
  • We offer a hub for collaboration and communication across disciplines. iLABMED unites researchers and clinicians from diverse medical subspecialities including artificial intelligence, bioinformatics, proteomics, materials etc, providing opportunities to exchange, collaborate, and explore together.
  • All contributions submitted for publication in iLABMED are single-blind peer reviewed by at least two experts in the field to ensure a high-quality peer review service.

Indexing Information

CAS: Chemical Abstracts Service (ACS)