AI Technical Challenges and Their Strategic Implications

Abstract

This chapter establishes that healthcare AI must not be treated as a solved technology. Persistent technical limitations – generalisability gaps, explainability deficits, and actionability limitations – are structural properties of current ML systems with direct strategic and patient safety consequences. The chapter introduces the three principal technical challenges as an analytical framework, operationalises distribution shift as the most clinically dangerous failure mode, and maps each challenge to its governance and monitoring response through the Strategic-Technical Alignment Framework. A five-stage monitoring and revalidation protocol provides the lifecycle management tool for live deployments. The regulatory landscape – MHRA Software as a Medical Device, FDA Predetermined Change Control Plan, and EU AI Act high-risk provisions – is examined to show how technical limitations create enforceable obligations. The framework is applied to vendor evaluation and procurement, providing requirements that translate technical analysis into contractual safeguards. The case study examines sepsis prediction AI failures across US hospital networks, tracing distribution shift and alert fatigue to derive an NHS-applicable response. By chapter end, leaders will understand AI governance as a technically necessary response to real system limitations.