Table 1 Potential AI application areas identified through semi-structured interviews from laboratory professionals in clinical chemistry practice

Big Data (patient information, test results, and instrument outputs) utilization for clinical service, education, and research

Budgetary planning

Decision support systems with AI algorithms using patient data and clinical guidelines

Delta checks and error detection for patient safety

Digital imaging and pattern recognition of electrophoresis gels and chromatograms

Enhance laboratory efficiency and decision making

Lean processes and workflow optimization

Managing inventory

Pattern recognition for diagnostic and prognostic assistance

Policy development (disease surveillance, epidemiology and allocation of resources based on disease burden)

Predictive models for diseases

Quality control data analysis to identify potential errors or inconsistencies

Reference range development through big data

Research on Big Data and AI (precision medicine, identification of novel biomarkers and development of biobanks)

Resources utilization through smart AI models

Total lab automation

Transforming diagnostic processes and improving patient outcomes via algorithms

Treatment response patterns with biomarkers