Digital Care and Artificial Intelligence in the Lab [LAB-12-EN]

Digital Care and Artificial Intelligence in the Lab [LAB-12-EN]

Laboratory medicine is undergoing a profound structural shift: clinical decision support algorithms, AI-assisted image analysis, and integrated laboratory information systems (LIS) are no longer aspirational technologies but operational realities that accreditation bodies — including those applying ASF and ISO 15189:2022 frameworks — now expect to find governed by explicit, auditable policies. Yet across Georgia and the broader South Caucasus region, many laboratories have acquired digital tools faster than they have developed the quality-management infrastructure to govern them, producing compliance gaps that external surveyors consistently flag during accreditation visits. Left unaddressed, these gaps carry direct patient-safety implications: unvalidated AI outputs, inadequate cybersecurity controls, and poorly documented algorithm change-management can each introduce systematic error into diagnostic workflows. This course was designed precisely to close that gap — translating international normative requirements into implementable laboratory practice.

The course is grounded in ISO 15189:2022 requirements for information management and process control, WHO guidance on artificial intelligence for health, and the European Commission’s regulatory framework for AI in medical devices (EU AI Act, 2024), contextualised for laboratories operating under Georgian national accreditation standards (ASF). Teaching follows a competency-based methodology: each module pairs normative standard analysis with annotated case studies drawn from real accreditation findings, enabling participants to move directly from conceptual understanding to documented procedural action. Formative scenario exercises reinforce critical thinking about AI validation, data governance, and audit trail construction — skills that are immediately transferable to a laboratory’s quality-management system.

By the end of this course, participants will be able to:

• Distinguish between AI-assisted decision support, AI-driven automation, and traditional algorithmic tools, and apply the correct regulatory classification to each technology present in their laboratory environment.
• Implement a structured AI validation protocol — covering analytical performance, clinical utility, and bias assessment — aligned with ISO 15189:2022 Clause 7.3 requirements for examination procedures.
• Design an auditable digital-care governance framework that assigns accountability for LIS configuration changes, algorithm updates, and incident reporting in accordance with ASF accreditation criteria.
• Evaluate cybersecurity controls and data-integrity safeguards within laboratory information systems against ISO/IEC 27001 baseline requirements and WHO recommended practices for health data protection.
• Construct standard operating procedures (SOPs) for staff competency assessment in the use of AI-enabled laboratory tools, including defined escalation pathways when system outputs are clinically ambiguous or technically suspect.
• Analyze accreditation survey findings related to digital care — drawn from published non-conformity data and Georgian ASF inspection patterns — and generate corrective action plans with measurable, time-bound performance indicators.

Who Should Attend

This course is designed for laboratory quality managers, clinical laboratory scientists, and medical directors who hold operational or governance responsibility for digital systems within accredited or accreditation-seeking laboratories. It is equally relevant for hospital chief medical officers and health informatics leads involved in procurement or oversight of AI-enabled diagnostic platforms. Professionals at mid-career and senior levels will derive the greatest practical benefit, particularly those preparing for an ASF accreditation cycle or responding to post-survey corrective action requirements.

Course Structure

The course is delivered entirely online through the GMJ Academy platform, comprising six structured modules with an estimated total study time of eight to ten hours; participants complete a scenario-based summative assessment requiring a minimum score of 75% to receive a certificate of completion. Successful graduates are awarded CME/CPD credits recognised by the Georgian Medical Association, and the certificate of completion is accepted as documented evidence of competency development for ASF quality-management file requirements.