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This review article examines the application of artificial intelligence (AI) technologies, including deep learning (DL), machine learning (ML), and medical big data analytics (MBDAT), in medical device research and development. It focuses on how AI can improve medical imaging, diagnostic aids, and drug response prediction devices. The review also discusses the current status, trends, challenges, and future directions of AI in medical device development.
AI technologies, particularly deep learning and machine learning, are poised to significantly enhance the precision, efficiency, and personalization of medical devices across imaging, diagnostics, and drug response prediction.
Medical devices are currently at a pivotal stage driven by policy and technological innovation. Traditional device development suffers from lengthy cycles, high costs, and poor alignment with clinical needs, making it difficult to adapt to rapidly evolving healthcare scenarios and high-end diagnostic demands. Breakthroughs in artificial intelligence (AI) technologies, particularly deep learning (DL), machine learning (ML), and medical big data analytics (MBDAT), offer critical solutions to this challenge: deep learning (DL) empowers the development of medical imaging devices, enabling precise lesion identification; machine learning (ML) supports diagnostic aids, intelligent decision support systems, and digital therapeutics, constructing personalized treatment and intervention models while optimizing data processing efficiency for laboratory biochemical analyzers; medical big data analytics drives innovation in drug response prediction devices, enhancing medication precision and safety through multi-source data integration and mining. This paper provides a systematic review of advancements in this field across three dimensions: specific AI applications in medical device R&D, current R&D status and trends enabled by AI, and existing challenges and future directions.
