Define the clinical job before selecting a vendor. Examples may include case prioritization, detection support, measurement, cell counting, biomarker quantification, quality control, or a second-read function. State the specimen type, tissue, stain, scanner, patient population, user, clinical setting, output, and decision the output is allowed to influence.

Confirm regulatory status and review the labeling, decision summary, indications, contraindications, warnings, compatible components, and required user qualifications. The FDA's AI-enabled device list is useful for transparency but is not comprehensive, and inclusion does not make a device suitable for every laboratory. Legal, regulatory, laboratory, and clinical leaders should verify current requirements for the specific use.

Separate clinical deployment from research. A research tool can be evaluated in a controlled protocol without allowing its output to direct patient care. Establish access controls, data separation, disclosure, oversight, and publication responsibilities. Prevent research outputs from appearing in the clinical record unless an approved workflow authorizes that use.

Write the value hypothesis in measurable terms. A tool might reduce time to review, improve consistency, identify a defined subset for closer attention, or reduce repetitive manual measurement. Avoid vague claims that AI will solve staffing shortages or eliminate diagnostic error. Technology can shift work and create new review, validation, and support duties.

Set stop criteria before procurement. Examples include unacceptable discordance, delayed turnaround, image failure, unsafe bias, cybersecurity risk, loss of vendor support, or inability to reproduce the validated configuration. A credible exit plan strengthens the business case because leaders know how patient care will continue if the tool is withdrawn.

Establish a registry of every deployed model, version, intended use, owner, vendor, validation, data flow, user group, and status. Link the registry to procurement, information security, laboratory quality, incident reporting, and change control. Untracked software embedded in a larger platform can otherwise escape governance.

Monitor performance after go-live. Review concordance, false positives, false negatives, unanalyzable cases, override patterns, turnaround, downtime, and patient-safety events. Use sampling that includes routine and challenging cases. Overall accuracy can conceal weak performance in a rare diagnosis, specimen type, stain, scanner, or demographic group.

Watch for input drift and operational change. New stains, reagents, scanners, compression settings, laboratory sites, specimen mix, or preparation practices can change the data reaching the model. A vendor software update can also alter output. Set thresholds for investigation, revalidation, temporary suspension, and rollback.

Require vendors to disclose relevant changes and support local monitoring. Contracts should address version notice, validation evidence, cybersecurity, vulnerability response, uptime, data rights, subcontractors, regulatory events, incident cooperation, export, and termination. Avoid terms that prevent the laboratory from investigating performance or retaining its own quality records.

Use NIST's AI risk functions as an organizing discipline: govern responsibility, map the context, measure performance and risk, and manage identified problems. The framework does not replace medical-device regulation, laboratory requirements, or clinical judgment, but it can help leaders create a repeatable enterprise process.