The Future of AI Enabled Medical Device Engineering: Integrating Predictive Analytics, Regulatory Automation, and Intelligent Manufacturing

Abstract

This essay explores the convergence of artificial intelligence (AI) and medical device engineering within three critical concepts: predictive analytics, regulatory automation, and intelligent manufacturing. When AI and its offshoots first arrived in healthcare in the mid-2010s, its prognosis was bleak. The software was error-prone, and its medical applications were both limited in variety and shallow in scope. Since then, systems engineering teams within medtech companies and health tech providers developed enhanced software and subsequently recorded meaningful gains in patient healthcare outcomes. Marketing campaigns introducing these success stories showcased any number of impressive anecdotes, and the narrative looking forward was bright. Better and better medical devices would continue to be produced because their engineering process was being heavily augmented by more and more advanced AI systems. In turn, greater patient demand would be fueled thanks to device availability, leading to cloud-connected redundancy. Meanwhile these predictive analytics driving the broader feedback loop would improve upon themselves reflecting a virtuous cycle. On top of all this, additive and computer-assisted fabrication techniques would mature and spread, facilitating the high-throughput production of medical devices both vast and varied. Considering the thousands of firms worldwide employing systems engineering and the prospect of mature AI—where such technologies are classified according to a fuzzy line—this essay evaluates the technology industrial base as impressively diverse and evolved; the focus here is on just a few representative trends.
However, the pundits' opinion on AI in medical device engineering in the early 2020s shared a far more sober perspective. Their narratives were replete with warnings about what could go catastrophically wrong, and in response companies debated what AI guardrails ought to be best agreed upon. That presence of concern was in part a reflection of what had already gone well astray. A recent rapid literature review study encompassed numerous articles published between 2015-2023. Initial AI-augmented medical devices and related software repeatedly arrived well behind schedule. Such products were often over-hyped publicly, leading to media backlashes and consequent contested regulatory approvals. A handful of companies suffered catastrophic injuries and costly recalls of software-controlled devices as a result of pervasive, well-documented failures, while medtech startups experiencing technical debt stalled growth or abruptly ceased operations entirely—most often in the under-regulated health tech sub-sector. Furthermore, projected gains in productivity were rarely as advertised, and in the absence of sound data management protocols these systems frequently encoded existing biases and prejudices in decision-making workflows..