Artificial intelligence

AI Applications in Health Sector: Use of Artificial Intelligence in Covid-19 Crisis and Impacts of Medical Robots on Global Economy

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Background:Artificial Intelligence (AI) is already in use in many fields of healthcare, and utilization of AI in state-of-the art healthcare services is growing, with many studies showing that AI can provide benefits in healthcare processes. However, there is a need for research about healthcare professionals’ willingness to use AI-based services, and their consideration about in which cases AI could provide better healthcare outcomes and reduce healthcare professionals’ workload.Methods:We studied the latest technologies and methods from current healthcare AI services’ end-users to create a survey on healthcare services, including use cases that could be beneficial for healthcare professionals. We focused on studying Information and Communication Technology (ICT) applications and services which utilize modern AI features. The purpose of our survey for healthcare professionals was to provide an analysis of the healthcare services that utilize AI features, which could provide better health outcomes from a healthcare professional and care process point of view.Results:AI features, such as health monitoring, medication management, and connected machines are considered to have high benefit in healthcare workflow. Moreover, we found that the majority (67.8%) of healthcare professionals are willing to use AI for supporting decision making or even providing independent diagnosis. In total, 28.9% of healthcare professionals are willing to use AI in a restricted manner to help professionals in care processes. Only 3.3% of respondents are not willing to use AI-based services at all.Conclusions:Our survey indicated that the willingness of healthcare professionals to use different AI in healthcare technology solutions is high.

Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

The integrations of Artificial Intelligence (AI) have generated tools that enhance the efficacy of various healthcare processes. AI-driven technology enables an accurate and early detection of diseases, personalizes treatment plans according to individual patient data, and streamlines administrative tasks. However, the study on the knowledge and perceptions among healthcare professionals in Malaysia was limited. Thus, this study was conducted to assess the knowledge and perceptions of AI in healthcare among healthcare professionals in a private setting in Malaysia. A cross-sectional study was conducted on 198 healthcare professionals in a private healthcare setting in Negeri Sembilan, from September 2023 until December 2023. A validated questionnaire comprised of demographic data and knowledge of AI was used. Descriptive analysis and Chi-square were used to analyze the data. Overall, most of the respondents (69.7%) had poor knowledge of AI with only 49.0% of the respondents defining AI as the use of computer algorithms to perform tasks that require human intelligence. Besides, the majority of the respondents (61.6%) are aware of the integrations of AI in the healthcare system. The majority of respondents (79.8%) have not yet utilized AI and lack experience in integrating AI into their daily work routines, however, the majority expressed a readiness to learn and incorporate AI into their professional practice (79.8%). Overall, the perceptions of AI among the respondents were moderate (70.2%). Hence, it is imperative to implement focused educational and training initiatives to tackle the diverse perspectives on Artificial Intelligence (AI) among healthcare professionals. Furthermore, it is crucial to thoroughly review and enhance the guidelines and policies regarding AI on the ethical framework in the hospital to ensure that AI is used ethically among healthcare professionals.

ObjectiveThe integration of Artificial Intelligence (AI) in healthcare offers opportunities to transform patient care, improve efficiency, and support clinical decision-making. Yet, its implementation is hindered by technical, organizational, and collaborative challenges. This study explores the key success factors for sustainable AI adoption from the perspectives of healthcare professionals (HCPs) and AI experts, with the aim of identifying alignments and gaps that influence collaboration and sustainability.MethodsGuided by the NASSS framework, semi-structured interviews were conducted with four HCPs and five AI experts with experience related to AI technologies in the healthcare sector. Thematic analysis was applied to examine stakeholder perspectives, focusing on gaps and alignments between stakeholders and identifying common patterns.ResultsTransparency leading to trust was the most emphasized factor by both AI experts and HCPs. Alignments were also found in recognizing the importance of interorganizational cooperation, demand-side value, scope of use, usability, responsibility and redefining the roles. Gaps included challenges in cooperation with HCPs, misunderstandings between stakeholders and the need for interdisciplinary experts, insufficient training, concerns over data quality and privacy, and limited attention to usability. Based on the found patterns, a framework is proposed to strengthen collaboration between AI experts and HCPs, enabling effective and sustainable AI implementation in healthcare.ConclusionThis study extends the NASSS framework to a dual-stakeholder context, offering insights into the alignments and gaps between HCPs and AI experts. The proposed framework supports improved collaboration, guiding healthcare professionals, AI developers, and managers toward effective, sustainable AI implementation and fostering mutual understanding for successful adoption.

In the current years, artificial intelligence is bringing a great revolution in different fields, especially in the field of medicine and healthcare. It is witnessing and gaining much importance which looked to be impossible few years ago. AI is like a boon to medical field and it is becoming increasingly important for doctors, radiologists, nurses and other healthcare professionals as well. The healthcare has become a basic need for humans in this current era and also became a big business today. The promise of AI in the field of medicine is to improve decision making and also to rectify errors such as misdiagnosis and unnecessary long procedures involved in healthcare systems. The various technologies like Internet of Things (IoT), cloud computing, edge computing, Artificial intelligence, big data analysis that are developed and are under process of development in the context of healthcare. The healthcare industry provides a huge amount of health data of patients where in machine learning algorithms could use this data for early detection of problems. The patients, doctors and the hospitals are looking forward for better health monitoring and management systems within stipulated amount of time and better efficiency. This paper briefs about various latest developments and technologies involved in healthcare and disease prediction systems using artificial intelligence carried out in recent years.

ObjectiveThis survey aims to understand frontline healthcare professionals’ perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions.Materials and MethodsWe conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants’ familiarity and experience with AI on the survey results.ResultsFrom 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = −0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups.DiscussionThe findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity.ConclusionThe study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals’ perspective. These insights can guide strategies for successful AI implementation in healthcare.

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.

BACKGROUND: With the increasing volume of data, laboratory medicine requires automation and standardization of routine processes to reduce workload on healthcare professionals and clear their time for more specialized tasks. Machine learning models and artificial neural networks support image recognition and analysis of large data sets, which allows their integration into laboratory workflows to solve routine tasks. AIM: This study aimed to analyze global scientific publications on the application of artificial intelligence technologies in laboratory medicine and their potential to address current challenges and identify barriers in their integration into laboratory workflows. METHODS: A search for publications was conducted using PubMed, manufacturer websites offering ready-to-use laboratory solutions, and reference lists from other reviews. The Mendeley software was utilized for bibliographic data management. The search covered the time interval 2019–2024. Obtained data included bibliometric indicators, research areas, key methodological characteristics, diagnostic effectiveness values for artificial intelligence systems and healthcare professionals, the number and experience of involved healthcare professionals, and validated outcomes of artificial intelligence implementation. The study quality was assessed using a modified QUADAS-CAD checklist. RESULTS: Twenty-three publications presenting studies at the pre-analytical (n = 1), analytical (n = 19), and post-analytical (n = 3) stages of laboratory analysis were included. Most studies focused on cytology and microbiology, accounting for 48% and 35% of the studies, respectively. Artificial intelligence demonstrated high effectiveness in solving tasks across all stages of the laboratory process. Moreover, its diagnostic accuracy was comparable to that of healthcare professionals; however, decision-making speed was higher. All studies demonstrated a risk of systematic bias, which was associated with unbalanced samples, lacking external data validation, and incomplete description of datasets and analytical methods. CONCLUSION: Artificial intelligence demonstrates high potential in diagnostic accuracy and processing speed, making it a promising tool to be integrated into laboratory practice and automation of routine processes. However, to achieve this, research methodologies for artificial intelligence should be standardized to reduce the risk of systematic bias, establish reference values for laboratories to ensure the reproducibility and generalizability of results, raise awareness among healthcare professionals and patients on how artificial intelligence works to overcome prejudices, and develop reliable mechanisms for protecting personal data when using artificial intelligence.

The research paper aims to study The Rise of Artificial Intelligence: A Boon or a Bane under Legal Regime. It will examine how the Artificial Intelligence (AI) in the real world refers in various domains and across various fields to boost innovation with transformative technology. The jurisprudential aspect highlights the philosophy of law concerning Artificial Intelligence along with its fundamental principles and moral values. It will analyse AI as a catalyst for progressing in today’s current era which symbolises the capacity of human intellect with AI through development in the modern day. AI as a positive footprint in the society understands the interaction of humans and AI. It will also examine the AI as a loss of human touch and its values where there is a gap between emotional connection between AI and human. The growing privacy challenges with regard to breach of personal data is a threat to human world. It will analyse the ethical and legal concerns of AI systems with lack of privacy and data protection. The growing dependence of AI poses challenges in security concerns. It will understand the AI in legal profession while providing legal services. It contrasts AI with major countries like The United States, The United Kingdom and The European Union as a comparative analysis. It will examine that how AI is truly a boon or a bane.

Background Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, enabling them to perform tasks. The advancements in AI have also improved virtual reality (VR), augmented reality (AR) and mixed reality (MR) experience allowing a greater opportunity for use in the field of medicine. Objective To evaluate the knowledge, attitude and practice of AI and types of realities among Pakistani healthcare professionals (HCPs). Materials and methods This was a prospective, nationwide study designed at the Department of Neurosurgery at Punjab Institute of Neurosciences (PINS), Lahore, was conducted between January 2024 to February 2024. More than 500 HCPswere approached, out of which 176 participated in this survey consensually. A pre-formed general questionnaire based on knowledge, attitude and practices of AI and types of realities was modified according to local conditions. Google Forms (Google Inc., USA) was used to conduct the one-time sign up response. Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 24, USA) was used to analyze submitted responses. Results About 69.9% respondents were male HCPs. Most of the respondents were from the fields of neurosurgery, medicine and general surgery, i.e., 10.80%, 10.20% and 4%, respectively. More than 90% HCPs used Internet and electronic devices daily. A majority of 62.50% respondents agreed that AI brings benefits for the patients, while at the same time, 45.50% agreed that they would not trust the assessment of AI more than that of HCPs. 61% HCPs feared that AI-based systems could be manipulated from the outside sources, like terrorists and hackers. Although 90% respondents knew the definition of AR and VR,a strikingly low 40% respondents could only identify the practical applications of these realities when asked in a mini-quiz. About 61.40% HCPs never used any AI-based application throughout their clinical practice, but Google Health was used by 29.50% respondents, followedby Remote Patient Monitoring AI application used by 3.4% individuals. Conclusion There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan. Lack of awareness, paucity of resources and conventional clinical practices are the key reasons identified. Pakistan is on the path towards the point where the developed world is currently. There is a potential to move past the initial stages of AI implementation and into more advanced modes of adopting AI and types of realities.

A BSTRACT Context: Materiovigilance ensures the safe use of medical devices by monitoring, detecting, and preventing adverse events associated with them. The integration of artificial intelligence (AI) into materiovigilance offers potential for enhanced real-time monitoring. However, healthcare professionals (HCPs) often exhibit gaps in knowledge, attitude, and practice (KAP) regarding AI in this domain. Aims: To assess and evaluate the change in KAP among HCPs regarding AI in materiovigilance before and after a structured educational intervention. Settings and Design: A pre-post interventional, questionnaire-based study was conducted among 80 HCPs at a tertiary care hospital. Subjects and Methods: A validated 24-item questionnaire was distributed via Google Forms. An information leaflet served as the intervention, designed using the BALD criteria and evaluated for readability (Flesch Readability Ease = 31, Flesch–Kincaid grade level = 10.5). Statistical Analysis Used: Pre- and postintervention data were statistically analyzed using the Wilcoxon signed-rank test, and effect size was measured using Cohen’s d . Results: The intervention led to significant improvements across all three domains. Awareness of materiovigilance rose from 53.75% to 97.5%, and knowledge about the Materiovigilance Programme of India increased from 62.5% to 100%. Positive attitudes towards AI’s role in reducing medical device-related errors improved from 47.5% to 100%. Practices such as participation in AI discussions and training showed significant gains ( P < 0.00001). Cohen’s d value of 1.10 indicated a large intervention effect. Conclusions: The educational intervention significantly improved HCPs’ KAP regarding AI in materiovigilance. Targeted training programs can bridge existing knowledge gaps and support AI integration in clinical device safety monitoring.

Purpose of review Artificial intelligence (AI) is increasingly prevalent in the clinical workplace, a trend that is likely to continue with the amount of attention and resources these technologies receive. This review of 22 articles from the last 18 months takes stock of not only the prospects but also the challenges for clinicians resulting from AI integration. Recent findings While the technology matures rapidly, insights into organizational processes and user readiness and involvement in AI development, implementation, and deployment lag behind. AI impact assessments often focus narrowly on task efficiency, overlooking the derived effect of additional workload elsewhere. Additionally, the issue of the distribution of responsibility between humans and AIs poses a fundamental ethical, legal, and political challenge. Research acknowledges the need to consider healthcare professionals’ diverse roles and sociocultural backgrounds to avoid AI exacerbating existing inequalities among the clinical workforce and, ultimately, the patients cared for. Summary Decision-makers should involve users throughout the entire AI life cycle, from the early stages of AI development to continuous postdeployment impact assessment on workload. More research is needed on AI's cost-effectiveness, integration into clinical practice, and the role of diversity-aware facilitation in realizing its potential.

This article explores the transformative role of artificial intelligence (AI) in healthcare medical education, highlighting the urgent need to integrate AI into medical curricula. It examines the current gaps in AI literacy among healthcare professionals, proposes practical teaching strategies, and discusses the ethical considerations essential for the responsible implementation of AI. An extensive literature review was conducted to identify key challenges and opportunities in integrating AI into healthcare education. Case studies, existing curricula, and frameworks were analysed to explore effective teaching methodologies, including problem-based learning (PBL), simulations, and ethical embedding. Multidisciplinary collaboration among academic institutions, technology companies, and government organisations was also examined as a pathway to bridge the gap between AI innovation and practical application. The findings reveal that while AI is rapidly becoming an integral part of healthcare practice, current medical curricula do not adequately prepare students for its adoption. Integrating AI into curricula through PBL, simulations, and real-world case studies fosters critical thinking and practical skills. Embedding ethical considerations throughout the curriculum addresses data privacy, bias, and accountability issues. Interdisciplinary partnerships and continuous professional development are key strategies for enhancing AI education and ensuring alignment with evolving healthcare needs. Reimagining healthcare education to incorporate AI is essential for preparing healthcare professionals to navigate an AI-driven future. Embedding AI into curricula, addressing ethical concerns, and fostering collaborations between academia, industry, and government can responsibly equip healthcare professionals to harness AI's potential. This paradigm shift is critical for improving patient care, advancing medical innovation, and ensuring the ethical integration of AI in healthcare systems.