Computing the Future of the Lab
Computational biology expert Keaun Amani shares his advice and insights on AI and machine learning tools for the clinical lab
Testing Trends-dtet
Dissecting the Data Behind Healthcare AI
How do the underlying datasets affect artificial intelligence tools’ performance in the lab—and beyond?
PGx Testing: First, Do No Harm
Recent advances in pharmacogenomic testing—and their role in ensuring safe, effective treatment selection for precision oncology
The Demand for Digital Pathology
When it comes to cancer, patients’ greatest fear is diagnostic delays—and they believe digital pathology and AI can reduce those delays
Syndromes Without a Name Bring Labs an Uphill Battle
In an era when genetic and genomic testing are theoretically affordable and accessible, why do so many patients remain undiagnosed?
Mind Reading: Lab Testing for Mental Health Conditions
What lab tests currently inform mental health care—and how might the testing landscape change as our understanding and technologies evolve?
MCED: A New Screening Paradigm?
Multicancer early detection tests—and their promise of comprehensive, minimally invasive screening—are catching the eye of labs and lawmakers
Removing Racial Inequalities in Lab Referrals
Clinical laboratory professionals must consider how race affects test referrals and what they can do to combat existing inequities
Pandemics: Prediction, Prevention, and Response
Recent efforts to forecast the next infectious disease outbreak and steps labs can take to ensure they’re prepared.
Sepsis: Diagnosis on a Deadline
New molecular tests and diagnostic support technologies can help labs detect sepsis earlier, faster, and with greater accuracy
CDC Releases Recommendations to Address Rising Syphilis Cases
Key updates from the CDC’s new lab recommendations for syphilis testing.
Automating Your Clinical Laboratory
How clinical laboratories can get started in automation—and what to consider when doing so.
Challenges and Opportunities in Lab Automation
As technology advances and testing demand grows, automation and AI are increasingly making their way into the clinical lab.
