Regulatory science for AI-based software as a medical device in computational pathology and biomarker-driven drug development.

BACKGROUND

Artificial intelligence (AI)-enabled software is increasingly integrated into digital and computational pathology, driving new regulatory and quality management requirements that extend beyond traditional laboratory practice and classical medical-device oversight. Practical, experience-based guidance on balancing development agility with global regulatory readiness remains limited for biomarker developers and translational pathologists navigating the convergence of AI governance, cybersecurity, and regulated clinical deployment.

METHODS

We reviewed our multi-year regulatory, quality, information security management program, and software lifecycle artifacts associated with AI-based diagnostic software development across multiple jurisdictions. Documentation, change control processes, and internal coordination mechanisms were analyzed to identify structural patterns supporting parallel progress in regulatory submissions, product releases, and assurance infrastructure.

RESULTS

Our assessment consistently showed four transferable operational determinants to maintain iterative development while preserving regulatory readiness across jurisdictions: (1) Regulatory submissions and approvals (e.g., IVDR, FDA clearance); (2) product releases and lifecycle control; (3) quality and assurance infrastructure, including quality management system (QMS) certifications (e.g., ISO 13485, MDSAP); and (4) cybersecurity and information security certifications (e.g., ISO 27001, HITRUST, and C5). Together, these determinants enabled coordination of regulatory, release, and quality milestones in parallel, reducing friction at later submission stages and supporting synchronized readiness across jurisdictions.

CONCLUSIONS

This technical note presents a transferable framework for managing AI-based pathology software development in regulated environments. High-quality deployment requires more than model performance alone; it depends on technical, regulatory, and operational maturity across many dimensions, including change control, documentation, security-aligned quality systems, post-market surveillance, technical support, and workflow integration. The presented framework is directly relevant to computational scientists, pathologists, and laboratory/medical directors tasked with evaluating AI systems by supporting informed evaluation and adoption decisions, including procurement considerations, in clinical and biopharma settings.