Artificial intelligence remained one of the most active areas of federal healthcare policy, and ADLM expanded its work to ensure laboratory medicine is represented in decisions about how these technologies are developed, validated, and used in patient care.
Earlier this year, ADLM submitted comments to FDA and HHS emphasizing that responsible AI policy must address not only algorithms, but also the quality, comparability, and clinical meaning of the laboratory data on which AI systems rely. ADLM also released a position statement calling for risk-based oversight, stronger laboratory data harmonization, validation and verification standards, bias mitigation, continuous performance monitoring, and appropriate laboratory professional involvement.
On July 10, ADLM published Artificial Intelligence in Laboratory Medicine: Leveraging Laboratory Expertise to Improve Patient Care, a policy report developed with laboratory medicine experts. The report recommends risk-based governance frameworks, independent verification and validation before clinical deployment, post-deployment monitoring, external quality assessment and benchmarking, stronger interoperability and harmonization, workforce development, and sustainable reimbursement policies.
The report also makes the case that laboratories should serve as foundational governance and operational partners as AI becomes more integrated into healthcare. Laboratory professionals already work within systems built around validation, quality assurance, regulatory compliance, data stewardship, and continuous performance monitoring, providing a practical foundation for responsible AI oversight.