Unlocking New Frontiers in Clinical Lab: AI driven Risk Stratification and Predictive Analytics

Artificial Intelligence is rapidly transforming clinical laboratories by enabling deeper insights from routine data. This special issue explores how association rule mining, a powerful AI technique, can reveal hidden patterns and correlations in complex datasets. By integrating predictive analytics into lab workflows, clinicians can anticipate disease trajectories and intervene earlier. Tools like clinical decision support systems are becoming more intelligent and responsive, helping doctors make faster, evidence-based choices tailored to each patient. As healthcare shifts toward precision and prevention, AI-based risk stratification allows for the identification of high-risk individuals even before symptoms manifest. These advances are not just technological—they’re reshaping how we understand, diagnose, and manage disease. Together, these innovations are unlocking new frontiers in laboratory medicine, where data becomes foresight and algorithms become clinical allies.

To explore how artificial intelligence is reshaping the role of clinical laboratories in modern healthcare. The focus is on moving beyond traditional diagnostics to a future where lab data drives prediction, prevention, and personalized care. By applying methods like association rule mining, researchers can identify subtle patterns and relationships within complex clinical datasets, offering early clues about disease risk and progression. Another key goal is to demonstrate how predictive models can be integrated into daily workflows to support faster and more accurate decisions. There's also a strong interest in developing tools that help clinicians stratify patient risk more effectively, even before symptoms appear. Ultimately, the issue seeks to bring together innovative work that shows how AI can help laboratories become more than just diagnostic hubs—transforming them into critical engines of foresight, guiding care teams in making timely and targeted interventions.

This Research Topic invites original research, reviews, and case studies that explore the application of artificial intelligence in clinical laboratories, with a focus on predictive analytics, risk stratification, and decision support systems. We welcome interdisciplinary contributions that demonstrate how AI can uncover novel disease patterns, enhance diagnostic accuracy, and support early interventions. Submissions should provide clear clinical relevance and explain how AI methods translate into improved patient care or operational efficiency. Authors are encouraged to highlight real-world applications, challenges, and future directions. All manuscripts will undergo peer review and must follow the journal’s submission guidelines. We especially encourage work that bridges data science and laboratory medicine, offering practical insights for both clinicians and researchers.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Association Rule Mining, Predictive Analytics, Clinical Decision Support, AI-Based Risk Stratification, Business Process Optimization

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.