人工知能と持続可能なオペレーション：医療機器製造業のためのフレームワーク

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AIを規制産業の持続可能なオペレーションに適用する際の統治・検証・スケールの研究ギャップを整理する枠組みを提供する。

🏢実務担当者:医療機器・精密製造のサステナビリティ担当は、予知保全やデジタルツインによる廃棄・エネルギー削減と、AI導入に伴うデータガバナンス負担の両面を検討できる。

🏛政策担当者:規制産業におけるAI導入の環境・倫理的負荷の移転を防ぐため、検証・ガバナンス要件の設計に示唆を与える。

Healthcare equipment manufacturing faces the dual imperative of maintaining product safety, regulatory compliance and quality while advancing sustainability across the product life cycle. This review conceptualizes artificial intelligence as a set of operational capabilities that can enhance predictive maintenance, process optimization, quality assurance, traceability, demand planning, circular design and life-cycle decision-making. At the same time, the paper shows that AI can create new sustainability problems through high computing demand, data governance burdens and regulatory complexity if adoption is poorly designed (Katirai, 2024; Rowan, 2024; Bignami et al., 2025). [1] The paper uses a PRISMA-informed systematic literature review, structured through SPAR-4-SLR logic and thematic analysis. The final corpus included 58 peer-reviewed publications from 2000 to June 2026, with a strong concentration after 2020, alongside selected policy and sector documents for contextual support on regulation and net-zero supply chains (Page et al., 2021; Paul et al., 2021). [2] The literature reveals six consistent findings: AI-enabled predictive maintenance reduces downtime, energy loss and unnecessary component replacement; digital twins enhance process visibility, design iteration and resource efficiency; AI-based quality systems support defect, scrap and compliance-risk reduction; circular economy models for medical devices are technically feasible but operationally underdeveloped; healthcare supply chains remain a major sustainability hotspot, highlighting the value of AI-enabled procurement and inventory management; and sector-specific evidence remains fragmented, with limited plant-level empirical research focused directly on healthcare equipment manufacturing. The main research gap, therefore, is not whether AI can support sustainable operations, but how to govern, validate and scale it in tightly regulated manufacturing environments without shifting environmental and ethical burdens elsewhere. [3]    

• semanticscholar https://asiaentrepreneurshipjournal.com/index.php/jaes/article/download/646/476first seen 2026-09-16 05:17:00 · last seen 2026-09-22 05:18:29