INTRODUCTION: An interdisciplinary panel, comprising professionals from medicine, AI and data science, law and ethics, and patient advocacy, convened to discuss key principles on regulation, implementation and evaluation of AI models in healthcare for Singapore. METHOD: The panel considered 14 statements split across 4 themes: "The Role and Scope of Regulatory Entities," "Regulatory Processes," "Pre-Approval Evaluation of AI Models" and "Medical AI in Practice". Moderated by a thematic representative, the panel deliberated on each statement and modified it until a majority agreement threshold is met. The roundtable meeting was convened in Singapore on 1 July 2024. While the statements reflect local perspectives, they may serve as a reference for other countries navigating similar challenges in AI governance in healthcare. RESULTS: Balanced testing approaches, differentiated regulatory standards for autonomous and assistive AI, and context-sensitive requirements are essential in regulating AI models in healthcare. A hybrid approach-integrating global standards with local needs to ensure AI comple-ments human decision-making and enhances clinical expertise-was recommended. Additionally, the need for patient involvement at multiple levels was underscored. There are active ongoing efforts towards development and refinement of AI governance guidelines and frameworks balancing between regulation and freedom. The statements defined therein provide guidance on how prevailing values and viewpoints can streamline AI implementation into healthcare. CONCLUSION This roundtable discussion is among the first in Singapore to develop a structured set of state-ments tailored for the regulation, implementation and evaluation of AI models in healthcare, drawing on interdisciplinary expertise from medicine, AI, data science, law, ethics and patient advocacy.
Singapore ; Humans ; Artificial Intelligence/standards* ; Delivery of Health Care/organization & administration*

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

It has attracted much attention worldwide that the application of artificial intelligence (AI) in primary screening and clinical diagnosis and treatment of eye diseases. In recent years, this technology has also been widely used in various grass-roots eye disease management, effectively improving the current situation of weak eye disease diagnosis ability and shortage of human resources in primary medical institutions. At present, there is no reference standard or guideline for the management mode, implementation content and management method of vision health management based on this technology, which are in urgent need of standardization. The article described the work mode exploration of AI-assisted grass-roots visual health management in Shanghai and shared practical experience. The aim is to provide reference for other provinces in China to carry out relevant work.
Humans ; Artificial Intelligence ; China ; Reference Standards ; Workforce

OBJECTIVE: To understand the current situation of artificial intelligence production enterprise quality management system, so as to provide reference basis for the research and standardization of Artificial Intelligence Medical Device (AIMD) product quality management. METHODS: Based on YY/T 0287-2017 Medical Device Quality Management System for Regulatory Requirements, Medical Equipment Production and the Quality Control Standard for Independent Software Appendix and Xavier GMLP report, the relevant factors were screened and the questionnaire was designed by combining expert consultation and literature review. Then, a total of 32 representative AIMD enterprises were invited to fill in the questionnaire. Descriptive statistical analysis was performed on the data results using Excel 2016. RESULTS: Through in-depth analysis of the four themes in product planning and design, result output, product quality control and product change, it was found that it was necessary for enterprises participating in the survey to improve the quality management system of AIMD products to different degrees. CONCLUSIONS This study is the first time to systematically investigate the status quo of quality management of AIMD enterprises. The result will be useful for the establishment and continuous improvement of product quality management system. It will also provide a reference for the research of AIMD product quality management and the establishment of the standard.
Artificial Intelligence ; Quality Control ; Reference Standards ; Software

According to the requirements for developing the quality control technology in Chinese medicine( CM) manufacturing process and the practical scenarios in applying a new generation of artificial intelligence to CM industry,we present a method of constructing the knowledge graph( KG) for CM manufacture to solve key problems about quality control in CM manufacturing process.Based on the above,a " pharmaceutical industry brain" model for CM manufacture has been established. Further,we propose founding the KG-based methodology for quality control in CM manufacturing process,and briefly describe the design method,system architecture and main functions of the KG system. In this work,the KG for manufacturing Shuxuening Injection( SXNI) was developed as a demonstration study. The KG version 1. 0 platform for intelligent manufacturing SXNI has been built,which could realize technology leap of the quality control system in CM manufacturing process from perceptual intelligence to cognitive intelligence.
Artificial Intelligence ; Drug Industry/standards* ; Drugs, Chinese Herbal/standards* ; Medicine, Chinese Traditional/standards* ; Pattern Recognition, Automated ; Quality Control ; Technology, Pharmaceutical

This paper presents a medical image acquisition and analysis method-TRM (Topology Resolve-Map) Model-under natural light condition indoors. Firstly, in accordance to medical image color characteristics, a colorful and grayscale color control patch was made for use as supervised color. "Topology Resolve-Map-Restoration" was carried on in LAB color space of the one-dimensional L* space and the two-dimensional a* b* space. Then, L* value was regulated by subsection regulation and a* b* value was regulated by triangulation topological cutting--close in on center of gravity method. After correction of the 198 color blocks in 22 pictures, the results showed that, by comparison with the standard value, the deltaL*, deltaC* and deltaE decreased significantly (P < 0.01) after correction by TRM. After correction, the difference in image's color is reduced, the color saturation is improved and the value is closer to true value. TRM model can significantly reduce the color difference of the medical image under natural light condition; it has a good effect on color correction.
Artificial Intelligence ; Calibration ; Color ; standards ; Humans ; Image Processing, Computer-Assisted ; methods ; Pattern Recognition, Automated ; methods ; Photography ; instrumentation ; methods

Our purpose is to introduce and analyze the data quality assurance (DQA) protocol of functional magnetic resonance imaging (fMRI). A water phantom was scanned to get DQA indexes. An fMRI sequence was used to get signal noise ratio (SNR) and Drift, which was calculated from maximum difference ratio of the average signal intensity in the region of interest (ROI) of image serials. The long period application of this method demonstrated that this DQA protocol can reflect imaging performance and the state of stability of the MRI scanner. Some application experience and discussion involved in DQA were also presented here.
Algorithms ; Artifacts ; Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted ; methods ; Magnetic Resonance Imaging ; methods ; Phantoms, Imaging ; standards ; Quality Control

This paper introduces the principium and application of the embedded intelligence control platform (EICP) in the clean operating room in our hospital. It can be a master of automatic control for air decontamination, temperature, humidity, lighting lamps, shadowless lamp, etc..
Artificial Intelligence ; Automation ; instrumentation ; methods ; Environment, Controlled ; Equipment Design ; Operating Rooms ; standards

Artificial Intelligence ; Conservation of Natural Resources ; Databases as Topic ; Drugs, Chinese Herbal ; standards ; Materia Medica ; standards ; Medicine, Chinese Traditional ; Pharmacognosy ; Quality Control ; Social Change