Artificial Intelligence Based Healthcare Device Development

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.