OBJECTIVE: It provides reference for accurate and efficient supervision of medical artificial intelligence industry. METHODS: By summarizing the main responsibility dilemma of medical artificial intelligence diagnosis products, sorting out relevant researches at home and abroad, the primary responsibility system of medical artificial intelligence diagnosis products is constructed. RESULTS: A medical artificial intelligence diagnosis products primary responsibility system with the marketing authorization holder as the "first responsible person" is established, and three safeguard measures are proposed, namely, algorithm transparency and interpretability, classification supervision mode and social co-governance supervision mode. CONCLUSIONS The medical artificial intelligence diagnosis products primary responsibility system is helpful to implement the primary responsibility, to build "responsible and beneficial" artificial intelligence, and to realize "self-discipline", "good governance" and "in good order".
Artificial Intelligence ; China ; Humans

OBJECTIVE: To modify the monitoring process and means of adverse events diagnostic reagents,improve the quantity and quality of adverse events reported ,and reduce the workload of regulatory authorities,eventually ensure the safety and effectiveness of diagnostic reagents. METHODS: The pre-filtering risk assessment system based on BP neural network was used to evaluate the adverse events of diagnostic reagents.According to the evaluation results,the administrative supervision departments took corresponding countermeasures. RESULTS: The BP neural network learned the historical data,and the risk evaluation results of the adverse events were basically consistent with the expert group. CONCLUSIONS BP neural network can be used to evaluate the risk of adverse events and achieve risk signal aggregation of adverse events.
Indicators and Reagents ; adverse effects ; Neural Networks (Computer) ; Risk Assessment

OBJECTIVE: To improve the monitoring mode of diagnostic medical devices adverse events. METHODS: By discussing the objective laws of the characteristics, performances and causes of diagnostic medical devices adverse events, the key points of monitoring work were clarified. RESULTS: The whole-process cloud monitoring mode for adverse events of diagnostic medical devices was constructed based on risk management, and the working procedures for the four core links i.e. collection and report, investigation, analysis and evaluation, and controlling were formulated. CONCLUSIONS The whole-process cloud monitoring mode contributes to improve the monitoring level and efficiency of diagnostic medical devices adverse events in China, so as to strengthen risk control capability and ensure the public can use medical devices safely.
China ; Equipment and Supplies ; Reagent Kits, Diagnostic ; Risk Management

OBJECTIVES: To solve the problem that medical device adverse event monitoring entities perform their duties inadequately, to provide reference for perfecting the post-market surveillance system. METHODS: Through theoretical and empirical research, the paper explored the ways to improve the performance of monitoring the adverse events of medical devices. RESULTS: The survey found that the number of adverse event monitoring reports was few and the quality of report was poor. The root causes included lack of motivation of monitoring entities, the imperfect monitoring system, and the monitoring capability failure, etc. CONCLUSIONS The methods such as strengthening the main body responsibility consciousness, establishing evaluation system and accountability system, building social work network, are beneficial to the adverse events monitoring.
Equipment Safety ; Equipment and Supplies ; adverse effects ; Product Surveillance, Postmarketing ; Surveys and Questionnaires

OBJECTIVESTo increase the number and quality of adverse events reported in medical devices, dealing with adverse events that have occurred in time, preventing the occurrence of adverse events, and ensuring the safety of device use.

METHODSBased on risk management methods, through a comprehensive analysis of risk of adverse events, scientifically assessing the risk level and completing the classification of adverse events. Administrative supervision departments take corresponding supervision measures according to the classification results.

RESULTSBuilding a classification monitoring model of medical device adverse events based on risk management.

CONCLUSIONSThe classification of adverse events will help the administrative supervision department to focus on the work, reduce the workload, and improve the efficiency of supervision.