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
Emerging Tests-dtet
Dissecting the Data Behind Healthcare AI
How do the underlying datasets affect artificial intelligence tools’ performance in the lab—and beyond?
Has Your AI Been Validated?
Many artificial intelligence-based medical devices have not undergone clinical validation—but what does this mean for the lab?
Expert Q&A: A Promising Future for Alzheimer’s Blood Tests
Alison Green, PhD, head of laboratory sciences at Scottish Brain Sciences, discusses recent developments in AD blood tests
Doing More with Less
A new capillary blood collection system, the BD MiniDraw™, can improve the testing experience for both labs and patients
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
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
At-Home Answers in Infectious Disease
Demand for at-home sample collection and infectious disease testing is at an all-time high—and still growing.
Screening Shortfalls and Self-Collected Samples
Is at-home sample collection the solution to lagging cancer screening uptake—or does this approach introduce a new set of challenges?
Advancements in Antimicrobial Susceptibility Testing: Part 2
Several methods are being developed that improve the ability to rapidly identify organisms and report susceptibility results.
Polygenic Risk Scores: A Cardiovascular Conundrum
Polygenic risk scores may offer new screening and risk stratification options for cardiovascular disease—but how valuable are they?
AI: With the Lab Every Step of the Way
Artificial intelligence models have a valuable role to play in each phase of the total testing process.
