Artificial intelligence ethics code in healthcare. Sustainability of artificial intelligence systems: Why do we talk about their impact on the environment?

Environmental problems have a tremendous impact on the entire world population, particularly on human health, which plays a leading role in individual well-being. Environmental pollution, according to some estimates, kills approximately 9 million people every year. The introduction of artificial intelligence (AI) systems in many areas has enormous potential in reducing human impact on the environment; however, such systems have negative effects. The potential of AI systems to improve healthcare is inextricably linked to the ethical challenges posed by the complexity of these systems and their impact on the lives and health of communities, patients, and staff. In addition to aspects that relate directly to the algorithms, data, and clinical application of AI systems, long-term risks exist that are not obvious at first glance. One of these risks is the negative impact of AI systems on the environment, which may harm human health indirectly. AI systems are more than software, having physical components that are necessary for their functioning, such as processors, memory, and sensors. The manufacture and the energy consumption of the components has a profound effect on the environment. One study showed that when a single AI algorithm is trained, carbon emissions may reach values corresponding to the total carbon emissions from five cars lifetime. This study analyzes existing literature linking the development of AI systems, especially in healthcare, to their effects on the environment. The study is intended to complement the emerging AI Ethics Code for healthcare, specifically the principles of sustainability that will be included in this code. The study concludes that the environmental impact of AI systems should be considered when formulating ethical standards for AI in healthcare. These standards must be considered during the development, testing, and application phases of AI systems. All the people involved in the creation and use of AI systems (developers, physicians, and regulators) must monitor the environmental impact and minimize the environmental consequences of such systems at all stages of their existence. This principle calls for minimizing negative impacts, improving the energy efficiency, and disposing physical components in strict compliance with current legislation. Moreover, the rapid development of AI systems and the ethical dilemmas require that solutions be proposed jointly and ethical standards be developed in a manner that is consistent and sensitive to emerging technologies

Comparison of the duration of generating radiological protocols with keyboard and voice input

BACKGROUND: Speech recognition is becoming increasingly common in the national healthcare system. One of the first specialties to implement this technology on a large scale was radiology. However, the efficiency of voice input and its effect on the length of time required to complete medical records remain unresolved. AIM: To assess the efficiency of speech recognition in generating radiological protocols of different modalities and types. METHODS: The retrospective study was conducted at the Moscow Reference Center of the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Department of Health. A total of 12,912 radiological reports on fluorography, mammography, chest computed tomography (CT), contrast-enhanced magnetic resonance imaging (MRI) of the brain, and contrast-enhanced CT of the abdomen and pelvis were included in the study by simple random sampling. The size of all samples exceeded 766 reports, calculated with regard to the size of the general population of over 100,000 reports. The Voice2Med software was used to fill in the radiological protocols. Intergroup comparison was performed using the MannWhitney U-test with a statistical significance level of 0.05. RESULTS: The average duration of generating fluorographic protocols in the keyboard and voice input groups was 189.9 s (0:03:09) and 236.2 s (0:03:56), respectively (p 0.0001). For mammographic reports, the duration was 387.1 s (0:06:27) and 444.8 s (0:07:24), respectively (p 0.0001). For radiographic reports, it amounted to 247.8 s (0:04:07) and 189.0 s (0: 03:09), respectively (p 0.0001), and for chest CT, it was 379.7 s (0:06:19) and 382.7 s (0:06:22), respectively (p=0.12). For MRI of the brain, the protocols were generated for 709.9 s (0:11:49) and 559.9 s (0: 09:19), respectively (p 0.0001), and for contrast-enhanced chest, abdominal, and pelvic CT scans, it took 2714.6 s (0:45:15) and 1778.4 s (0:29:38), respectively. Voice input slowed down the preparation time of mammographic and fluorographic protocols. This is due to the use of a structured electronic medical document in medical facilities to describe the results of the examinations. Speech recognition showed the greatest efficiency in generating MRI and CT protocols. Such reports contain a large number of pathological changes, both target and incidental findings, which requires a detailed description by the radiologist in the examination protocol. CONCLUSIONS: Speech recognition in generating radiological protocols showed different efficiency depending on the modality and type of the radiological protocol filled in using the voice input system. This approach is optimal for describing CT and MRI scans

Quality management system: A tool for the development of the organization or an additional burden?

A quality management system constitutes one of the organization’s management systems that provides for the selection of a set of processes in the organization’s activities designed to ensure the stable quality of products and services provided. The growth of global industrial production has underscored the need for the creation of such production and management systems. These systems are designed to ensure that enterprises remains prepared to meet the constantly changing consumer value of manufactured products in accordance with consumer requirements, as well as the satisfaction of consumers themselves. As a result, attention began to focus on the production processes implemented within the organization when creating products. Regarding the production of medical devices, a quality management system can be defined as an organizational structure encompassing its functions, procedures, processes, and resources necessary for the coordinated direction and management of a manufacturing organization with respect to the quality of medical products. The article reflects the principles of the quality management system and management processes. Noteworthy emphasis is placed on the features of quality management systems for medical devices, including the features of the quality management system for software that is a medical device. Furthermore, the conditions under which the quality management system becomes a tool for ensuring the sustainable development of the organization are noted