Aug 2026· Frontiers in Industrial Engineering· Vol 4· 0 citations· 57 references
TL;DR
This scoping review synthesizes the literature to construct a four-stage developmental framework for medical HFE, delineate four interconnected core human–machine collaboration challenges, and clarify the prevailing theoretical, methodological, and translational constraints.
Abstract
Against the ongoing intelligent transformation of global healthcare, human factors engineering (HFE) is essential to the safe deployment of intelligent medical devices. Yet existing reviews largely offer chronological summaries and fragmented, scenario-specific analyses, leaving the field’s paradigm evolution and cross-scenario human–AI challenges under-synthesized. Following the PRISMA-ScR guideline, this scoping review synthesizes the literature to construct a four-stage developmental framework for medical HFE, delineate four interconnected core human–machine collaboration challenges, and clarify the prevailing theoretical, methodological, and translational constraints. It further identifies future directions—human–AI collaborative reliability evaluation, data-driven usability testing, and digital-twin-supported full-lifecycle validation. By integrating paradigm evolution, core challenges, and regulatory translation into a single analytical framework, this work provides a structured foundation for subsequent research, human-centered design, and regulatory evaluation of intelligent medical devices.
This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· International Journal of Inn...· 0 citations
PURPOSE
Medical devices are a mainstay in modern healthcare, supporting the diagnosis, monitoring, treatment, and management of acute and chronic conditions. Rapid technological innovation, increasing software dependence, growing connectivity, and the emergence of artificial intelligence (AI)-enabled systems have expan...
Shabnam Vaezzadeh, Charles Oyesile, Nicole Barber et al.· Clinical Therapeutics· 0 citations
BACKGROUND
Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces significant challenges in scalability, objectivity, and dynamic adaptability. The integration of Artificial Intelligence (AI) is catalyzing a paradigm shift from "experience-dri...
Hao Wu, Ya-Li Yang, Li Wang et al.· Journal of Evaluation In Cli...· 0 citations
Background/Objectives: The integration of artificial intelligence (AI) in medical devices has significant potential for transforming healthcare in the European Union (EU). However, AI medical devices are regulated under the Medical Devices Regulation (MDR) and the In Vitro Diagnostic Medical Devices Regulation (IVDR),...
Guilherme Semedo, Emanuel Valpaços, L. Teles et al.· Healthcare· 0 citations
Introduction. Cardiovascular diseases remain a leading cause of mortality, disability, and reduced quality of life, supporting the need for more accessible and consistent digital approaches in cardiac rehabilitation.Aim. To develop and functionally verify a patient-centred digital clinical decision support system proto...
V. Horoshko, S. Myronenko, S. Nakonechna· Ukrainian journal of cardiov...· 0 citations
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