Synthetic Datasets and Medical Artificial Intelligence Specifics

Currently, artificial intelligence is entering a rapidly developing era. Artificial intelligence technology is growing rapidly. Artificial intelligence technology can already be applied to various fields of social life, and artificial intelligence products with artificial intelligence technology as the core are abundant. Artificial intelligence products have been integrated into the lives of ordinary people. Due to the autonomy of artificial intelligence products, they may work in an unmanned state at certain times, resulting in infringement liability that may cause personal or property damage to others during the operation of artificial intelligence products. There are two ways to pursue accountability: one is to directly pursue the infringement liability of artificial intelligence products, and the specific subject who caused the infringement of artificial intelligence products shall bear the infringement liability; another approach is to pursue the responsibility of the producer and seller of artificial intelligence products for their subsequent observation obligations. The reason is that the producer and seller of artificial intelligence products fail to fulfill their subsequent observation obligations, resulting in the failure to detect defects in the artificial intelligence products in a timely manner and resulting in subsequent observation obligations. The responsible parties are the producers and sellers of artificial intelligence products.

(Potential) users of artificial intelligence (AI) products (e.g., smart fridges, voice assistants) are most often not fully informed about the data security and data privacy (DSDP) of these AI products as such information is not available in an easily accessible format. Here, I argue that it is essential to provide (potential) users with such information presented in an easily accessible and quick-to-process format as this could help instill appropriate levels of (dis-)trust and thus support (trustworthy) AI acceptance and adoption. Testing the effect of AI trustworthiness labels, I presented participants with hypothetical AI products that were versus were not paired with a graphical or text-based label indicating a low to high DSDP level. Trust, AI anxiety, as well as the monetary value attributed to AI products scaled with an AI trustworthiness label’s DSDP level. Importantly, participants’ ratings of unlabeled AI products corresponded to their perceptions of AI products labeled with an intermediate DSDP level. This apparent bias towards intermediate DSDP judgements in the absence of information on AI products underscores the relevance of explicitly communicating AI trustworthiness to (potential) users. Interestingly, differences in trust for AI products with a high as compared to intermediate DSDP level further increased with more positive attitudes towards AI technology.

Riding on the "east wind" of ChatGPT's explosion, the copyright issues involved in artificial intelligence products have once again become the focus of academic circles. At present, the number of artificial intelligence products is proliferating, and the lack of protection of artificial intelligence products under the copyright law has led to multiple risks such as untrue attribution of artificial intelligence products and irregular regulation of plagiarism of artificial intelligence products. The practical demand for copyright protection of artificial intelligence products needs urgent response from the legal level. As the logical starting point for granting copyright protection to artificial intelligence products, there are two kinds of differences in the determination of the originality of the products, namely the personality binding theory and the objective standard theory. From the perspectives of legislative spirit, copyright development trend and technical feasibility, it is justified, feasible and positively meaningful to adopt the latter to grant copyrightability to artificial intelligence products. Based on the evaluation of the value of the copyrightability of artificial intelligence products, the originality criterion is broken down into two major elements, namely, uniqueness and creativity, to build a factual judgment path for the copyrightability of artificial intelligence products, and then to realize the specific determination of the copyrightability of specific artificial intelligence products. This in turn gives rise to the dispute over the copyright ownership of artificial intelligence products. At this stage, allowing copyright rights and interests to be freely distributed among relevant subjects is an appropriate way to create the best benefits and maintain a balance of interests of all parties.

Since the emergence of ChatGPT in 2022, generative artificial intelligence has brought rapid changes across industries and daily life. Because artificial intelligence model performance depends on the quality and scale of training data, companies are building large-scale training datasets by utilizing publicly available personal information and personal information of users they already possess. However, legal uncertainty persists as it remains unclear whether the processing of artificial intelligence training data containing personal information is lawful under personal information protection laws. Accordingly, this paper examines the concept, characteristics, and privacy risks of artificial intelligence training, and reviews interpretations regarding the processing of artificial intelligence training data, focusing on publicly available personal information and personal information of existing users, while analyzing their limitations. Ultimately, to achieve a balance between personal information protection and artificial intelligence industry development, it is urgent to clarify the lawful basis for processing artificial intelligence training data containing personal information and establish legal standards that can guarantee the rights of data subjects. Through this, it will be possible to resolve legal uncertainty for businesses, protect the right to self-determination of personal information, and promote trust-based development of the artificial intelligence industry.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

Dear Editors, I was very interested in your editorial [1]. Technological advancements are so rapid that humans are unable to keep up. And we eagerly await the outcomes of technological advancement. The study of artificial intelligence is one of the topics that has recently gained popularity. If someone had predicted a hundred years ago, "A technology will come that will detect the commands you receive, act accordingly, and write scientific articles," we would laugh at her. Many artificial intelligence dreams that appear weird to us now, I believe, will come true shortly. The Generative Pre-Trained Transformer 3 (GPT3) was developed a few years ago, in line with improvements in artificial intelligence. With this model, artificial intelligence was used to generate content that resembled human-written documents. A more advanced version of ChatGPT was produced a few years later. In March 2023, the most recent GPT4 version was launched. Errors in article writing have been reduced using this and comparable applications such as artificial intelligence. In fact, it has become nearly impossible to detect the difference between publications written by scientists and articles written by artificial intelligence. Some journals have begun to accept artificial intelligence apps as co-authors [2]. Article authoring is made considerably easier by artificial intelligence. In terms of time savings, using artificial intelligence products such as ChatGPT and GPT 4 may make sense. However, there are some risks associated with this circumstance. For example, because disseminating personal data is a felony, authors may feel compelled to conceal some information in order to preserve personal data. Artificial intelligence programs can readily perpetrate crimes involving personal data by releasing secret facts. This situation presents numerous legal issues. Furthermore, the language to be used in article writing may alter depending on the topic of study. In this instance, it is vital to go check the words chosen by artificial intelligence programs in the article. As a result, my recommendation is to use tools such as ChatGPT, GPT 4, or similar basic jobs once the user has written the article themselves. Leaving all of the work to these apps may cause more harm than good. Kind Regards

The digital age provides, on the one hand, extraordinary opportunities for modern information technologies, including artificial intelligence (AI) products; on the other hand, it creates new problems for humanity, for example, of a legal and ethical nature. In addition, AI products can be highly effective and attractive from a commercial point of view; and from the point of view of humanity as a whole, they can signify a huge problem. Thus, there is a need to balance the value of these products, taking into account their multiaspectual nature. The purpose of this study is to develop a conceptual approach to assessing and balancing the value of AI products. Balancing involves assessing the value of AI products, comparing it to some standards, and adjusting it based on the identified inconsistencies. It is found that balancing the value of AI products should take into account three areas of view and evaluation – individual, business, society (humanity). Such an integral view will provide coverage of the main levels that are affected by AI products. A method for assessing the value of AI products has been proposed, which takes into account the multiaspectual nature of product consideration for each area (individual, business, society) and the priority of each aspect. It is also proposed that in the future, it is artificial intelligence that should provide this assessment of the value of AI products, taking into account a significant amount of necessary information. A conceptual scheme has been formed that links the increase in the level of development of AI products with the level of their responsibility, especially for those products that are designed to evaluate and determine the necessary areas of harmonization and balancing of value. This will ensure the development and commercial use of AI products with risk minimization from various perspectives. It is evident that these results are only a conceptual approach, on the basis of which it is possible to develop specific algorithms for evaluating and balancing the value of AI products.

Artificial intelligence products (AIPs) for older adults enhance the functions of traditional products and improve the quality of their lives. However, AIPs are not popular among this population, and limited attempts have been made to investigate these users’ negative tendencies regarding AIPs. This study explores the causes of avoidance and exit behaviors toward AIPs among older people from both a functional and socio-emotional mismatch perspective. Data were collected from 1102 older AIP users to verify the research model and hypotheses. The results indicate that perceived control and expectation disconfirmation affect the functional mismatch, while public stigma has the greatest impact on the socio-emotional mismatch. Furthermore, the results highlight a mixed influence of the functional and socio-emotional mismatches on negative behaviors. This study explores older people’s negative tendencies toward AIPs, comprehensively considering the functions of AIPs and the socio-emotions they evoke. Thus, it provides new empirical evidence for the systematic relationship between the functional mismatch and the socio-emotional mismatch and fills the research gap on the influence on the subsequent behaviors of older adults. Additionally, this study sheds light on the specific methods of designing, developing, and promoting AIPs.

Aim. The work aimed to study the consequences of implementing products using artificial intelligence technologies in economic security systems.Objectives. The work seeks to analyze the development directions and results of artificial intelligence technologies; to assess the capabilities (beneficial effects) of these technologies in economic security systems of organizations; to identify specific threats to economic security, associated with the use of artificial intelligence technologies in organizations; to develop recommendations for mitigating these threats.Methods. The study employed comparative and retrospective analysis, economic-statistical and index methods, as well as special methods for studying economic security, expert-analytical and system approaches.Results. It has been established that the use of artificial intelligence in economic security systems can produce a significant beneficial effect. The advantages of its use in the field under consideration are associated with the fact that in formal analysis (from the standpoint of its rate, accuracy, consistency and taking into account the maximum possible number of factors), the artificial intelligence products surpass human capabilities. This results in a gradual replacement (moreover in the future) of human intelligence with artificial intelligence in the field under consideration, at least in cases of tasks that do not require non-standard, creative solutions, based sometimes on the paradoxical nature of human thinking. At the same time, such a development of events gives rise to a number of specific threats to economic security, namely “engagement” of many products using AI technologies; the possibility of manipulating information; loss of critical thinking skills by officials; the growing importance of cyberattacks.Conclusions. The development of digital technologies, including artificial intelligence technologies, has an ambiguous impact on economic security at all levels of the economic system. Overcoming the problems identified in the study requires activities in a number of fields, namely the formation of national products using artificial intelligence technologies, within the implementation of state policy to achieve technological sovereignty; strengthening the information component of systemic measures to ensure economic security; revision of approaches to the formation of competencies of specialists in the field of economic security, who use (or can potentially use) artificial intelligence products in their activities.

In recent years, the continuous development of artificial intelligence has brought convenience to people's lives but has also given rise to a series of problems. Among these, the gaps in laws related to artificial intelligence has sparked significant controversy, particularly with regard to copyright protection of artificial intelligence products, which has become a focal point of debate in theoretical and practical circles. Some scholars advocate for the protection of artificial intelligence products through copyright, while others argue against it. Based on this, the typical cases have been sorted out and analyzed, relevant provisions of copyright law have been studied, and the issue of the copyright for artificial intelligence products has been explored from both practical and theoretical perspectives. Additionally, in combined with scholars' viewpoints, the copyrightability of artificial intelligence-generated material is examined from the aspects of originality and intellectual achievement. The purpose of establishing the copyrightability of such material is to align with the development trend of the times and, simultaneously, to promote the enhancement of China's copyright legal system in order to foster cultural prosperity and development.

Several countries, including the UK, have approved commercial artificial intelligence (AI) products as medical devices to assist in the diagnosis of skin cancer. However, lack of robust real-world data has led to professional bodies recommending against their use outside research environments. The aim of this study was to assess the performance of an internationally proven deep neural network as a screening tool in patients referred from primary care on the suspected skin cancer pathway. Dermoscopic images were obtained from patients attending a community lesion imaging clinic following referral from primary care on the suspected skin cancer pathway. Ultimate diagnosis was based on histology where available, face-to-face clinical opinion if no histology was obtained, and the opinion of two dermatology specialists with over 10 years’ experience for patients discharged directly from teletriage without histology or face-to-face review. Images were assessed by the neural network and binary categorization was performed. The algorithm was set to detect invasive malignancies and premalignant conditions (actinic keratosis, Bowen disease, keratoacanthoma) and was trained to dismiss benign naevi, benign keratotic lesions (seborrhoeic keratosis, lichen planus-like keratosis, solar lentigines), dermatofibromata and benign vascular lesions. In total, 333 of 382 lesions from consecutive patients who gave research consent were included. Reasons for exclusion were pen artefact on dermoscopic image (26), lesion resolved prior to imaging clinic (eight) inappropriate site (six), poor image quality (five) and other (four). Ultimate diagnoses were basal cell carcinoma (62), squamous cell carcinoma (19), melanoma (11), other malignancy (one), benign keratotic (80), actinic keratosis (61), Bowen disease (20), benign naevus (17), benign vascular (nine), keratoacanthoma (eight) and other benign (45). Overall, 81 of 333 lesions (24.3%) were classified by the algorithm as benign and 252 (75.7%) were flagged as possibly malignant or premalignant. Sensitivity for cancer was 100% (93 of 93 detected). Sensitivity for premalignant conditions was 97% (86 of 89 detected). Specificity for all patients was 51.7% (78 of 151 benign lesions correctly classified). Specificity when removing the ‘other benign’ category, which is in the algorithm’s exclusion criteria and not part of its training, was 65.1% (71 of 109 lesions accurately categorized). Two actinic keratoses and one cases of Bowen disease were incorrectly classified as benign. The algorithm performed extremely well on this real-life case series and detected 100% of invasive malignancies. AI screening of suspected skin cancer referrals is likely to play a significant part in addressing future National Health Service capacity–demand mismatch and would have reduced the caseload requiring specialist opinion by 24.3% in this series. There is urgent need in the UK for further transparent clinical trials and open competition between AI products on large real-world datasets. Further stratification of benign and premalignant conditions may lead to greater benefit than binary classification.

The effect of medical artificial intelligence innovation locus on consumer adoption of new products

Artificial intelligence products are already around us and will be emerging dramatically a lot in near future. Artificial intelligence is all about data analysis. When it comes to data analysis, there are two representative techniques: machine learning and semantic technology. They stand on the other side from where to begin analysis. Simply speaking, machine learning is based on the data while semantic technology relies on human domain knowledge (human learning). What if collected data are insufficient to reflect whole phenomenon? This is a limitation of machine learning. What if circumstance changes a lot as time goes by? Manual rule updating by experts is not a good solution in that circumstance. Based on these observations, we investigate two approaches and find a good solution which maximizes the advantages of both techniques and mitigates the limitations of them. This paper suggests a novel integration idea to compensate each technology with the other: that is semantic filtering. This paper includes a toy semantic modelling and a machine learning algorithm implementation to realize the proposed concept, semantic filtering.

At present, although artificial intelligence technology is still in the stage of weak artificial intelligence, it depends on the development of digital computing, which breaks the monopoly position of human beings in the field of literary creation, and then the legal subject and behavior of copyright have intelligent tendency, which puts forward new problems in judicial practice. Through the analysis of the nature of AI-generated works and the distinction between different categories, this paper makes a concrete analysis of the copyright of AI-generated works, and then draws the conclusion that the copyright of AI-generated works belongs to people. By using the criterion of "contact + substantial similarity", it is concluded that the self-editing articles produced by artificial intelligence based on deep learning do not belong to infringement. The expression and utilization of manuscript washing in artificial intelligence products constitutes infringement and does not belong to fair use. Whether the use of the original material by the ideological use of manuscript washing constitutes infringement requires a case judgment.