Potential Impact Of Artificial Intelligence Research Articles

A policy framework on AI usage in developing countries and its impact

The rapid growth of Artificial Intelligence (AI) presents both significant opportunities and challenges for developing countries. A well-structured policy framework is crucial to maximize the benefits of AI while mitigating its risks. This review proposes a comprehensive AI policy framework tailored to developing countries, emphasizing the need for robust infrastructure, capacity building, ethical governance, and economic incentives. Key elements include the development of digital infrastructure, education and training programs to enhance AI literacy, and ethical guidelines to ensure fairness and transparency in AI applications. Data governance and privacy protections are critical, particularly in countries where regulatory frameworks are underdeveloped. Furthermore, international cooperation is highlighted as a necessity for aligning local policies with global AI standards, facilitating cross-border data sharing, and ensuring equitable access to AI innovations. The potential impact of AI on economic growth, job creation, healthcare, education, and public service delivery is profound, yet challenges such as workforce displacement, increased inequality, and the digital divide must be carefully managed. The proposed framework addresses these challenges, providing strategies to overcome barriers to AI adoption, including financial constraints, governance issues, and unequal access to technology. Moreover, it stresses the importance of fostering public-private partnerships and ensuring that AI development is inclusive, benefiting all segments of society. By implementing a comprehensive AI policy framework, developing countries can harness AI’s transformative power to drive sustainable development, improve social outcomes, and strengthen their economic standing in the global landscape. This review concludes by recommending continuous policy evaluation and adaptation to keep pace with AI's rapid evolution.

Conceptualizing the Implications of Artificial Intelligence (AI) Tools and Personalization Marketing on Consumer Purchase Intention: Insights from the Malaysian E-Commerce Market

Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA). This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.

The Role of AI in Shaping Our Future: Super-Exponential Growth, Galactic Civilization, and Doom

The present study investigates the potential impact of artificial intelligence (AI) on the future trajectory of human civilization. It focuses on topics such as super-exponential growth, the potential emergence of a galactic civilization, and the associated "doom" hazards. A significant advancement in machine intelligence with human-like consciousness, strong artificial intelligence (AI), also known as artificial general intelligence (AGI), creates new opportunities and capacities. There's growing anxiety about the risk that weak AI will eventually become strong AI. Every year, new transformer models that are more like human interactions are being created, and we have already witnessed some indications of AGI. It is anticipated that AI will reach a "singularity" and advance on its own without assistance from humans. This thesis explores the theoretical and practical foundations, model building blocks, development processes, challenges, and ethical issues surrounding the creation of Consciousness AI (AGI). This paper examines the meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order. As a new perspective on the deployment of ethical AI in the face of tremendous technological advancements, the study not only contributes to the theoretical discourse but also explores the possible practical implications of AI on our shared future. Several obstacles to AI advancement are covered in the paper, along with prospective directions for future research.

SWOT analysis of the application of artificial intelligence in radiologic technology and radiology

Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.

How Artificial Intelligence Could Affect the Future of Architectural Design Education

There is currently a high level of uncertainty about the potential impact of artificial intelligence (AI) on architecture as a profession and consequently on architectural education. Some suggest that schools of architecture should prepare themselves for a dramatic reform that clearly integrates AI technologies into their study plans and pedagogical methods, while others argue that AI can’t fully replace the traditional methods and conventions in architectural education and that more time is needed to understand its potential impact in this regard. In all cases, AI is coming and will be soon or later an integral part of our work and teaching methods in architecture. This paper discusses this issue considering some AI applications in the different design stages from an educational perspective. The study concluded that there is an urgent need to explore and understand how AI could affect our educational systems and provoke changes in the architectural design process we currently follow. Quality assurance organizations should reflect this in their accreditation guidelines to offer the required guidance for the schools of architecture in this regard.

Integrating Artificial Intelligence and Advanced Genomic Technologies in Unraveling Autism Spectrum Disorder and Gastrointestinal Comorbidities: A Multidisciplinary Approach to Precision Medicine

This article explores the potential impact of Artificial Intelligence (AI), Machine Learning (ML), CRISPR-Cas9 gene editing, and single-cell RNA sequencing on improving our understanding and management of Autism Spectrum Disorder (ASD) and its gastrointestinal (GI) comorbidities. It examines how these technologies illuminate the complex interplay between the gut and the brain, identifying specific enzyme deficiencies and microbial imbalances linked to GI symptoms in ASD. By leveraging AI and ML, personalized intervention strategies are developed through the analysis of genomic, proteomic, and environmental data, enhancing our ability to predict and address GI issues in ASD. Additionally, CRISPR-Cas9 gene editing holds promise for correcting genetic abnormalities related to enzyme production, potentially offering precise treatments. Single-cell RNA sequencing provides critical insights into the cellular diversity of the ASD gut, uncovering new therapeutic targets. The article highlights the transformative potential of these technologies while addressing the associated challenges and ethical considerations. It underscores the necessity of a multidisciplinary approach to fully harness their benefits and discusses the significant progress and emerging trends in the field, emphasizing the role of technological advancements in advancing precision medicine for ASD and its GI comorbidities.

Physical therapists’ perceptions and attitudes towards artificial intelligence in healthcare and rehabilitation: A qualitative study

Artificial intelligence (AI) is being introduced to rehabilitation practices, and it can optimize the patient's outcome through their ability to design personalized care strategies and interventions. To understand the attitudes and perceptions of physical therapy professionals on the use of AI in rehabilitation in regard to treatment planning, diagnosis, outcome prediction, and advantages and disadvantages. This paper followed an exploratory, qualitative research design. Semi-structured, one-to-one interviews were conducted with participants of different experience levels and specialties in physical therapy. Results were evaluated using thematic analysis. Four themes were identified: (i) perceptions of AI and its applications in healthcare services, (ii) impact on the workforce (iii) considerations around implementing AI within rehabilitation and (iv) AI, and the fast-approaching future. Participants shared views on the potential impact of AI on rehabilitation practices, such as aiding the decision-making process, saving time and effort of both the therapist and patients. Participants have stressed on potential pitfalls that still need to be considered, such as patient data privacy, potential loss of patient-healthcare practitioner relationship, ethical concerns regarding overreliance on these applications and how that might hinder effective patient care. The findings add to the literature about physical therapists' understanding regarding the use of AI in patient care. Several concerns were raised to the adoption of AI, including concerns about patient privacy, and ethical concerns. Based on the study findings, researchers emphasize the importance of establishing guidelines when incorporating AI in rehabilitation to improve the therapist's knowledge and skills.

Radiologists’ perceptions on AI integration: An in-depth survey study

PurposeTo assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. MethodsA survey was conducted among members of the SIRM Lombardy. Radiologists’ attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists’ attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. ResultsThe survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists’ opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI’s significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI’s relevance to future radiologists’ career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. ConclusionOur results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI’s potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.

Navigating Through Disruption: How are School Principals Leading Digital Learning in Secondary Schools in Ireland

The COVID-19 pandemic forced school leaders to rapidly adopt digital technologies, sparking new awareness and discussions about digital education and its transformation. Despite significant advances, these changes may be minor compared to the potential impact of artificial intelligence and immersive technologies in education. This paper explores digital learning leadership in Ireland’s secondary school sector using the ISTE Standards for Educational Leaders as a guiding conceptual framework. Qualitative interviews with school principals from various secondary school types provide insights into their existing leadership practices and anticipate their future needs. Key findings revealed digital learning visions integrally woven into the school’s broader learning and educational vision and fresh assessments of student digital competence. They highlighted the salience of the school context in decision-making and the drive for more strategic deployment of formal leadership positions and relationships within and between schools. The implication of these findings underscores the imperative of professional learning models for school leaders that are sensitive to the unique context of each school, their bespoke opportunities and challenges, and the rapid advancements in digital technologies.

“I know it’s a deepfake”: the role of AI disclaimers and comprehension in the processing of deepfake parodies

Abstract Rapid innovations in media technologies have ushered in diverse entertainment avenues, including politically oriented content, presenting both novel opportunities and societal challenges. This study delves into the implications of the burgeoning deepfake phenomenon, particularly focusing on audience interpretation and engagement with deepfake parodies, a quintessential example of “misinfotainment.” Additionally, it examines the potential impact of artificial intelligence (AI) disclaimers on audience understanding and related consequences. To probe this, two experiments (N = 2,808) were executed featuring parodied politicians adopting opposing viewpoints on the issue of climate change. U.S. participants were either exposed to deepfake videos prefaced with AI disclaimers or without. Results indicate that the inclusion of an AI disclaimer significantly influenced audience comprehension and their ability to recognize the parody. These factors were subsequently associated with enjoyment, discounting, and counterarguing, which in turn showed different relationships with policy support and sharing intentions. This article culminates with insights into the theoretical underpinnings and practical ramifications of these findings.

Artificial Intelligence and MCS Innovation

Abstract Upon the request and guidance of SC 6/AG 4 Convenor Kingston ZHANG, a research group has been formed for study the potential impact of Artificial Intelligence on MCS Innovation and to evaluate how can AG 4's work contribute to the next stage of SC 6/WG 7's Future Network standardization program. This is a preliminary report.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis.

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Knowledge, Attitude and Practice Toward Artificial Intelligence Among Healthcare Workers in Private Polyclinics in Jeddah, Saudi Arabia.

The objective of our study was to assess awareness, attitudes, and practices regarding artificial intelligence (AI) among healthcare workers in private polyclinics in Jeddah, Saudi Arabia. We conducted cross-sectional study among healthcare workers in private clinics in Jeddah. Data was collected using a structured, validated questionnaire in Arabic and English on awareness, attitudes, and behaviors regarding AI. Cronbach's alpha for the questionnaire ranged from 0.6 to 0.8. Descriptive and bivariate analysis was done to assess the scores and their association of various sociodemographic variables with awareness, attitudes, and behaviors regarding AI. Multiple linear regression was performed to predict the scores of awareness, attitudes, and behaviors based on the sociodemographic variables. We recruited 361 participants for this study. Approximately, 62% of the healthcare workers were female. The majority (36%) of healthcare workers were nurses, while 25% were physicians. The median awareness, attitude, and behavioral scores were 5/6 (IQR 3-6), 5/8 (IQR 4-7), and 0/3 (IQR 0), respectively. Approximately three-fourths (74%) of the healthcare workers believed that they understood the basic computational principles of AI. Only half of the participants were willing to use AI when making future medical decisions. We found that male healthcare workers had better knowledge scores regarding AI as compared to female healthcare workers (Beta = 0.555, 95%, p value = 0.010), while for attitude scores, being administrative employee as compared to other employees was found to have negative attitude towards AI (Beta = 0.049, 95%, p value = 0.03). We found that healthcare workers had an overall good awareness and optimistic attitude toward AI. Despite this, the majority is worried about the potential consequences of replacing their jobs with AI in the future. There is a dire need to educate and sensitize healthcare workers regarding the potential impact of AI on healthcare.

From education to practice—2024 update: An opinion paper of the Drug Information Practice and Research Network of the American College of Clinical Pharmacy

AbstractDrug information specialists (DIS) bring unique, specialized expertise and provide services in diverse settings including health systems, academia, pharmaceutical industry, compendia, medical writing, and other areas. With widespread access to drug information (DI) resources through user‐friendly, online platforms, the role of DIS has shifted. Core skill sets once confined to DIS are now distributed across various non‐DIS clinical specialties. DIS have transformed the application of their specialized skill set and adapted it to a variety of traditional and nontraditional areas, providing and applying advanced expertise to solve a variety of contemporary challenges. The training of students and residents has evolved to include evidence‐based practical contemporary methods that promote critical thinking and reasoning. Effective DI evaluation and communication necessitates customizing content for stakeholders to ensure understanding and contribute to optimal patient care, all while addressing misinformation and disinformation. The future of DI as a specialty is bright, with ever‐increasing recognition of the importance of DI skills in non‐DIS practitioners. DIS will likely continue to guide best practices in the education/training of new practitioners and continue to provide advanced services and formulary analytics. This update to our 2009 DI PRN Opinion paper will focus on: (1) DI education and training needed for all students, residents, and pharmacists; (2) career opportunities, roles, and responsibilities specifically tailored for DIS in health systems, managed care organizations, academia, pharmaceutical/biotechnology industry, and medical writing services; and (3) the future direction of DI, including the potential impact of artificial intelligence.

How does artificial intelligence promote renewable energy development? The role of climate finance

Scholars, stakeholders, and the government have given significant attention to the development of renewable energy in recent times. However, previous research has failed to acknowledge the potential impact of artificial intelligence on advancing renewable energy development. Drawing insights from a global dataset encompassing 63 countries over the period 2000–2019, this paper provides significant observations regarding the influence of artificial intelligence on the progress of renewable energy, by using the Instrumental Variable Generalized Method of Moments model. We also explore their asymmetric nexus, and the potential mediation effect. Moreover, this study explores the moderating role of climate finance and highlights the following interesting findings. First, artificial intelligence contributes significantly to the enhanced development of renewable energy, and this primary finding holds after two robustness tests of changing independent and dependent variables. Second, artificial intelligence has an asymmetric effect on renewable energy development, and their nexus is closer in countries with lower levels of renewable energy development. Thid, artificial intelligence works on renewable energy development through technology effect and innovation effect. Fourth, climate finance also presents direct benefits to renewable energy development; simultaneously, climate finance plays an effective moderating role in the relationship between artificial intelligence and renewable energy development. These findings inspire us to propose policy implications to promote the enhanced development of renewable energy.

Artificial Intelligence in Wound Care Education: Protocol for a Scoping Review.

As healthcare continues evolving in the age of digital technology, the integration of artificial intelligence has emerged as a powerful force, particularly in wound care. The education of healthcare professionals in wound care is crucial for ensuring they acquire the necessary knowledge and skills, optimizing patient outcomes. This paper outlines the protocol for a scoping review with the goal of mapping and analyzing the current scientific evidence regarding the potential impact of artificial intelligence in wound care education. The current protocol follows the JBI methodological framework. The search was conducted in December 2023 in the following databases: CINAHL Complete (via EBSCOhost), MEDLINE (via PubMed), Cochrane Library, Academic Search Complete, Scientific Electronic Library Online (Scielo), Scopus, and Web of Science. Electronics searches were conducted in the Scientific Open Access Scientific Repositories of Portugal (RCAAP) and ProQuest Dissertations and Theses, OpenAIRE, and Open Dissertations databases to access gray literature. Additionally, searches were performed on Google Scholar and specific journals such as the International Wound Journal, Skin Research and Technology, Journal of Wound Care, and Wound Repair and Regeneration. The initial database searches retrieved a total of 11,323 studies. After removing duplicates, a total of 6450 studies were submitted for screening. Currently, 15 studies are included in this review, and data charting and analysis are underway. The findings of this scoping review will likely provide insights into the application of artificial intelligence in wound care education.

Reimagining learning through AI art: the promise of DALL-E and MidJourney for education and libraries

Purpose This paper aims to explore the potential impact of artificial intelligence (AI) image generators, specifically MidJourney and DALL-E, on education and library services. The study aims to understand how these tools can revolutionize learning experiences and library resources while also addressing the ethical considerations surrounding their use. Design/methodology/approach This study investigates the technical foundations of MidJourney and DALL-E, highlighting their neural network architectures. It also traces the iterative refinement of these models and examines cost, accessibility and the unique prompt-guided capabilities of DALL-E 3. Findings MidJourney and DALL-E show remarkable progress in generating high-quality, photorealistic images from text prompts. The iterative refinement of these models demonstrates a trend toward improved creative output and user accessibility. DALL-E 3, in particular, allows users to guide image generation through prompt modifications, offering unprecedented control over the creative process. The study identifies potential applications in personalized learning, visual communication and research support in libraries, while recognizing challenges such as cost and accessibility. Originality/value This research innovatively explores AI's impact on education and libraries, detailing applications in personalized learning and research while addressing legal and ethical considerations.

Artificial intelligence in respiratory care: Current scenario and future perspective.

This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.

Defining Medical AI Competencies for Medical School Graduates: Outcomes of a Delphi Survey and Medical Student/Educator Questionnaire of South Korean Medical Schools.

Given the increasing significance and potential impact of artificial intelligence (AI) technology on health care delivery, there is an increasing demand to integrate AI into medical school curricula. This study aimed to define medical AI competencies and identify the essential competencies for medical graduates in South Korea. An initial Delphi survey conducted in 2022 involving 4 groups of medical AI experts (n = 28) yielded 42 competency items. Subsequently, an online questionnaire survey was carried out with 1,955 participants (1,174 students and 781 professors) from medical schools across South Korea, utilizing the list of 42 competencies developed from the first Delphi round. A subsequent Delphi survey was conducted with 33 medical educators from 21 medical schools to differentiate the essential AI competences from the optional ones. The study identified 6 domains encompassing 36 AI competencies essential for medical graduates: (1) understanding digital health and changes driven by AI; (2) fundamental knowledge and skills in medical AI; (3) ethics and legal aspects in the use of medical AI; (4) medical AI application in clinical practice; (5) processing, analyzing, and evaluating medical data; and (6) research and development of medical AI, as well as subcompetencies within each domain. While numerous competencies within the first 4 domains were deemed essential, a higher percentage of experts indicated responses in the last 2 domains, data science and medical AI research and development, were optional. This medical AI framework of 6 competencies and their subcompetencies for medical graduates exhibits promising potential for guiding the integration of AI into medical curricula. Further studies conducted in diverse contexts and countries are necessary to validate and confirm the applicability of these findings. Additional research is imperative for developing specific and feasible educational models to integrate these proposed competencies into pre-existing curricula.

The Role of AI in Advancing Sustainable Human Resource Management Practices

Purpose of the Paper: The purpose of this research paper is to explore the potential of artificial intelligence (AI) in promoting sustainable workplaces, specifically in the context of green HR. The motivation behind this study is the growing interest in environmental sustainability and the need for organizations to adopt sustainable practices. The integration of AI in HR practices has the potential to promote sustainability and reduce the carbon footprint of organizations. Problem Identified: The problem addressed in this paper is the limited understanding of the potential of AI in promoting sustainable workplaces, specifically in the HR domain. The paper aims to provide an in-depth analysis of the potential benefits and challenges of incorporating AI in HR practices to promote environmental sustainability. Approach: The approach taken in this study involves a thorough review of relevant literature on AI, green HR, and sustainable workplaces. The paper discusses the role of AI in promoting sustainable workplaces, including identifying patterns of energy consumption and reducing waste through AI-powered energy management systems such as automated lighting and heating systems and smart building technologies. Results: The paper summarizes the benefits of adopting sustainable workplace practices, including reduced costs, improved employee health, and enhanced corporate reputation. It also highlights case studies of companies that have successfully implemented sustainable workplace practices. The challenges of adopting AI for green HR are discussed, including cost, security, and privacy concerns, and the need for skilled AI professionals. The potential impact of AI on job displacement and the need for reskilling and upskilling are also analyzed. Strategies for implementing AI for green HR are presented, including best practices for effective change management, stakeholder engagement, and collaboration with AI vendors and suppliers. The importance of ethics and transparency in AI implementation is also discussed. Conclusion: In conclusion, the paper presents key findings and recommendations for companies seeking to implement AI for green HR. It reflects on the future of AI for sustainable workplaces and potential areas for future research. The study provides valuable insights into the potential of AI in promoting sustainable workplaces and serves as a foundation for further research in this area. Keyword: AI, Human Resource, Sustainable