Advocacy - Lab Advocate
On February 10, 2026, ADLM released a new position statement calling on Congress and federal health agencies to modernize laboratory regulations and adopt clear, risk-based guardrails for artificial intelligence (AI) in laboratory medicine. The statement argues that while AI has significant potential to improve diagnostic accuracy, streamline workflows, and support more precise clinical decision-making, those benefits depend on the quality and comparability of the laboratory data used to train and validate models.
The association warns that limited, low-quality, or inconsistent data can reduce model performance, limit generalizability across sites and patient populations, and introduce bias into clinical applications. The position statement also highlights that variation across assays, platforms, and calibration methods can create problems for AI tools that assume laboratory values are directly comparable, underscoring the need for stronger laboratory data harmonization efforts as AI becomes more embedded in healthcare.
To address these concerns, ADLM supports a policy framework that builds on existing laboratory quality systems rather than creating duplicative new requirements. Specifically, the statement calls for policymakers to modernize CLIA to explicitly address AI and machine learning systems, establish validation and verification standards for AI tools used in laboratory medicine, advance harmonization of laboratory data, clarify stakeholder roles and responsibilities, and support ongoing performance monitoring after deployment. The association also emphasizes that laboratories need sufficient access to relevant data and model information from developers so laboratorians can independently verify performance and monitor for drift, bias, and other emerging issues over time.
ADLM believes laboratory medicine professionals can play an essential role in ensuring AI is implemented responsibly, in ways that strengthen patient care rather than introduce new risks. The association remains committed to advancing thoughtful, evidence-based AI policy and working with policymakers and stakeholders to ensure these technologies are integrated into healthcare in a way that is accurate, equitable, and clinically meaningful.