Artificial intelligence and nonoperating room anesthesia.

The increasing rate of cardiovascular diseases (CVDs) has posed a tremendous challenge to their early detection and personalized treatment. This research examines the potential of Artificial Intelligence (AI) for early detection and management of CVDs, in particular whether it can enhance diagnostic accuracy, personalize treatment guidelines, and reduce healthcare costs. A quantitative methodology was adopted and a survey strategy was employed for collecting primary data from 300 healthcare professionals consisting of cardiologists, general physicians, and professionals in AI fields from Punjab hospitals in Pakistan. The questionnaire was constructed to determine their knowledge, experiences, and perceptions regarding the use of AI in cardiovascular services. Data analysis revealed that application of AI had a strong correlation with increased diagnostic success, evident in a statistically significant chi-square test (p < 0.001). Furthermore, multiple regression analysis revealed that AI, together with years of experience and educational history, is an important contributor to personalizing cardiovascular treatment plans. The results indicate that AI has a key role in making more precise diagnoses and improving treatment methods, which can ultimately decrease the cost of healthcare and enhance patient outcomes. Yet, issues around data privacy, transparency, and clinician confidence in AI systems must be resolved in order for AI to be adopted more widely. Future research is suggested by the study into the integration of AI with other health technologies and the ethics of using AI in clinical practice.

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

This study explored physician anesthesiologists' knowledge, exposure, and perceptions of artificial intelligence (AI) and their associations with attitudes and expectations regarding its use in clinical practice. The findings highlight the importance of understanding anesthesiologists' perspectives for the successful integration of AI into anesthesiology, as AI has the potential to revolutionize the field. A cross-sectional survey of 27,056 US physician anesthesiologists was conducted to assess their knowledge, perceptions, and expectations regarding the use of AI in clinical practice. The primary outcome measured was attitude toward the use of AI in clinical practice, with scores of 4 or 5 on a 5-point Likert scale indicating positive attitudes. The anticipated impact of AI on various aspects of professional work was measured using a 3-point Likert scale. Logistic regression was used to explore the relationship between participant responses and attitudes toward the use of AI in clinical practice. A 2021 survey of 27,056 US physician anesthesiologists received 1086 responses (4% response rate). Most respondents were male (71%), active clinicians (93%) under 45 (34%). A majority of anesthesiologists (61%) had some knowledge of AI and 48% had a positive attitude toward using AI in clinical practice. While most respondents believed that AI can improve health care efficiency (79%), timeliness (75%), and effectiveness (69%), they are concerned that its integration in anesthesiology could lead to a decreased demand for anesthesiologists (45%) and decreased earnings (45%). Within a decade, respondents expected AI would outperform them in predicting adverse perioperative events (83%), formulating pain management plans (67%), and conducting airway exams (45%). The absence of algorithmic transparency (60%), an ambiguous environment regarding malpractice (47%), and the possibility of medical errors (47%) were cited as significant barriers to the use of AI in clinical practice. Respondents indicated that their motivation to use AI in clinical practice stemmed from its potential to enhance patient outcomes (81%), lower health care expenditures (54%), reduce bias (55%), and boost productivity (53%). Variables associated with positive attitudes toward AI use in clinical practice included male gender (odds ratio [OR], 1.7; P < .001), 20+ years of experience (OR, 1.8; P < .01), higher AI knowledge (OR, 2.3; P = .01), and greater AI openness (OR, 10.6; P < .01). Anxiety about future earnings was associated with negative attitudes toward AI use in clinical practice (OR, 0.54; P < .01). Understanding anesthesiologists' perspectives on AI is essential for the effective integration of AI into anesthesiology, as AI has the potential to revolutionize the field.

Informed consent is fundamental to ethical medical practice, ensuring that patients understand the procedures they undergo, the associated risks, and available alternatives. The advent of artificial intelligence (AI) in healthcare, particularly in diagnostics, introduces complexities that traditional informed consent forms do not adequately address. AI technologies, such as image analysis and decision-support systems, offer significant benefits but also raise ethical, legal, and practical concerns regarding patient information and autonomy. The integration of AI in healthcare diagnostics necessitates a re-evaluation of current informed consent practices to ensure that patients are fully aware of AI's role, capabilities, and limitations in their care. Existing standards, such as those in the UK's National Health Service and the US, highlight the need for transparency and patient understanding but often fall short when applied to AI. The "black box" phenomenon, where the inner workings of AI systems are not transparent, poses a significant challenge. This lack of transparency can lead to over-reliance or distrust in AI tools by clinicians and patients alike. Additionally, the current informed consent process often fails to provide detailed explanations about AI algorithms, the data they use, and inherent biases. There is also a notable gap in the training and education of healthcare professionals on AI technologies, which impacts their ability to communicate effectively with patients. Ethical and legal considerations, including data privacy and algorithmic fairness, are frequently inadequately addressed in consent forms. Furthermore, integrating AI into clinical workflows presents practical challenges that require careful planning and robust support systems. This review proposes strategies for redesigning informed consent forms. These include using plain language, visual aids, and personalised information to improve patient understanding and trust. Implementing continuous monitoring and feedback mechanisms can ensure the ongoing effectiveness of these forms. Future research should focus on developing comprehensive regulatory frameworks and enhancing communication techniques to convey complex AI concepts to patients. By improving informed consent practices, we can uphold ethical standards, foster patient trust, and support the responsible integration of AI in healthcare, ultimately benefiting both patients and healthcare providers.

The article explores the rapidly growing role of artificial intelligence (AI) in reshaping legal practice, analyzing its potential applications, benefits, and the challenges that come with integrating AI into the legal field. As AI technology advances, it offers unprecedented opportunities to streamline legal processes, enhance efficiency, and empower legal professionals with powerful new tools. AI's capabilities in areas such as contract analysis, legal research, document review, and predictive analytics have the potential to significantly transform how legal work is conducted. The article examines the current state of AI adoption within legal practice and discusses its role in automating routine tasks, thus allowing lawyers to focus on more complex, value-added aspects of their work. However, the integration of AI into legal practice also brings about several challenges. Ethical concerns are paramount, particularly in areas such as algorithmic bias, transparency, and accountability. AI systems, which are often considered "black boxes," can make decisions that lack explainability, raising concerns about fairness and justice in legal proceedings. The article explores how AI may impact decision-making processes, especially in areas where human judgment has traditionally been paramount, such as in sentencing or dispute resolution. Furthermore, data privacy and the security of sensitive legal information are significant issues that need to be addressed when utilizing AI in legal practice. In addition to ethical and legal concerns, the article discusses the regulatory landscape surrounding AI in legal contexts. Various jurisdictions are beginning to consider how best to regulate AI's use in legal practice, with some adopting frameworks to ensure AI applications meet ethical and legal standards. The author explores ongoing efforts in Europe, the United States, and other regions to develop policies that address AI's implications in the legal sector. Another key point discussed is AI's potential to increase access to justice. By automating tasks, reducing costs, and improving the availability of legal services, AI could help bridge the gap in access to legal resources, particularly for individuals and businesses with limited financial means. AI systems could democratize legal knowledge and provide cost-effective legal advice, making legal services more accessible to a wider population.

Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.

Artificial Intelligence (AI) is increasingly discussed as a tool that can support speech and language therapy (SLT). However, clinical adoption of AI requires improved AI literacy among clinicians. AI is a rapidly evolving and often inconsistently defined field that can be difficult to navigate. Despite the definition provided by the EU AI Act, AI terminology can feel abstract for non-technical readers. To provide a foundational understanding of AI tailored for SLTs, by translating complex concepts into accessible language and organising them across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI can support SLT). This tutorial is informed by foundational AI literature, established AI taxonomies, relevant SLT literature and regulatory and ethical guidelines. Clinical analogies are used to explain technical concepts, with additional technical detail signposted where relevant. Existing and conceptual examples illustrate the relevance of AI across paediatric SLT practice. This tutorial provides: (i) a clinician-focussed interpretation of the EU AI Act definition; (ii) an organisation of key AI concepts into techniques, capabilities and clinical applications; (iii) a production-line model for mapping clinical needs to AI design choices and (iv) a practice-focussed discussion of ethical and regulatory considerations. AI is best understood as a set of techniques that enable specific capabilities, which in turn support clinical applications. This tutorial promotes the safe, ethical and accountable use of AI as a tool that can support rather than replace clinicians. What is already known on this subject Current Artificial Intelligence (AI) literature is typically designed for technical audiences, making it difficult for clinicians to interpret. This can hinder the effective and responsible integration of AI into clinical practice. What this paper adds to the existing knowledge This tutorial provides a clinician-focussed explanation of AI, structured across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI supports practice) in paediatric speech and language therapy. It also addresses key challenges, ethical considerations and regulatory requirements relevant to clinical contexts. What are the potential or actual clinical implications of this work? This tutorial lays the groundwork for informed engagement with emerging AI tools. It prepares clinicians to evaluate how different AI techniques and capabilities may support core clinical tasks (e.g., assessment, therapy planning and delivery).

Objective: To assess the knowledge and attitude of undergraduate nursing students towards the role of artificial intelligence (AI) in healthcare, aiming to understand their readiness and perception of integrating AI into clinical practice. Methods: A descriptive cross-sectional study was conducted to assess the knowledge and attitude of nursing students toward the role of Artificial Intelligence (AI) in healthcare. A total of 208 students were selected using non-probability convenience sampling technique. Informed consent was obtained from all the participants prior to the data collection. The study consisted of two parts: a 10-items knowledge questionnaire and a 10-items attitude questionnaire, designed to evaluate students' understanding of AI technologies and their perspectives on its integration into healthcare settings. The questionnaires were close-ended, focusing on basic knowledge about AI. Results: There was a significant difference in AI knowledge and attitudes between various groups. Male’s demonstrated significantly higher AI knowledge (82.1%) compared to females (69.8%) with a p-value of 0.003. Participants who attended formal AI training exhibited better knowledge, with 41.9% showing adequate knowledge, compared to 25.4% of non-attendees (p = 0.010). Prior exposure to AI workshops significantly influenced attitudes, with attendees showing a more positive attitude toward AI (67.4%) compared to non-attendees (35.8%), with a p-value of &lt;0.001. Gender and formal AI training were found to significantly impact both knowledge and attitude towards AI in healthcare. Conclusion: The study highlights significant differences in AI knowledge and attitude among undergraduate nursing students, with males, participants with formal AI training, and those exposed to AI workshops demonstrating higher levels of knowledge and more positive attitude. These findings underscore the importance of incorporating AI education and training into nursing curricula to better prepare students for the integration of AI in clinical practice.

Artificial intelligence (AI) has been rapidly evolving in medicine. While there are existing data about the perceptions and concerns of health care workers regarding AI, those of the hematology and oncology (HemOnc) workforce remain unknown. We assessed opinions of physicians (faculty and fellows), advanced practice providers (APPs), and nurses in HemOnc on integrating AI in medical education (MedEd) and clinical practice (CP). Group-specific electronic surveys were developed. The HemOnc workforce from the three Mayo Clinic sites was invited to participate. Participation was anonymous, with data collection between November 7, 2024, and January 20, 2025. Results were reported as proportions, with 95% binomial CI when appropriate. A total of 344 participants responded, 118 physicians (77 faculty, 41 fellows), 49 APPs, and 177 nurses. Most had used AI although less than half felt very or moderately knowledgeable about it. A total of 95% of fellows, 97% of faculty physicians, 96% of APP, and 92% of nurses believe that AI will be integrated in MedEd. A total of 94% of physicians and 88% of nonphysicians believe that this integration would be beneficial. In CP, 98% of fellows, 96% of faculty physicians, 100% of APPs, and 92% of nurses believe in AI's integration. However, 51% of physicians and 68% of nonphysicians had some concerns, including technology time, medical costs, and burnout. Nonetheless, 85% of fellows, 94% of faculty physicians, 98% of APPs, and 86% of nurses would embrace AI. The majority of this single health system HemOnc's workforce endorsed AI integration into MedEd and CP but highlighted relevant concerns. Proper education, effective partnership among all stakeholders, and well-organized integration are essential for optimizing AI's incorporation into HemOnc's MedEd and CP and ensuring that it fully supports the mission of providing high-quality patient care.

Study question What are the key considerations, validation frameworks, and safety guidelines required for the responsible implementation of Artificial Intelligence (AI) systems in MAR clinics? Summary answer The Croatia Consensus establishes internationally agreed-upon best practices for AI validation in MAR, ensuring patient safety, clinical excellence, regulatory compliance, and ethical implementation. What is known already AI applications are increasingly integrated into ART to optimise embryo selection, standardise clinical decision-making, and reduce variability. However, absence of internationally accepted validation frameworks, regulatory guidelines, and ethical oversight poses risks to patient safety and clinical efficacy. Current AI models often lack transparency, generalisation, and robust external validation. Bias in training datasets can lead to inequitable clinical outcomes. The need for structured AI governance in ART is pressing. The Croatia Consensus, formed by global experts (AI Fertility Society), aims to define best practices for AI validation and deployment in MAR clinics. Study design, size, duration A structured Delphi process involving 148 AI and MAR experts was conducted in 2024 to develop international guidelines for AI validation in ART. The consensus methodology included systematic literature reviews, expert panel discussions, and iterative feedback rounds. Topics covered included AI safety, validation protocols, data standardisation, regulatory compliance, and bias mitigation. The final consensus document was reviewed at the AI Fertility Society Meeting and endorsed by multidisciplinary stakeholders, including clinicians, embryologists, ethicists, and AI developers. Participants/materials, setting, methods Consensus guidelines were developed through contributions from embryologists, reproductive specialists, AI researchers, and regulatory experts. The process included a systematic review of AI applications in MAR, gap analysis of existing validation frameworks, and expert recommendations on AI validation strategies. Key aspects included standardised AI reporting (TRIPOD+AI compliance), real-world clinical validation across multiple centres, ethical risk mitigation, and transparent AI decision-making. AI system performance benchmarks were established using clinical outcome measures and patient safety indicators. Main results and the role of chance The Croatia Consensus establishes a comprehensive framework for AI validation in MAR, ensuring patient safety, regulatory compliance, and clinical efficacy. Key recommendations include multi-centre external validation of AI models to ensure generalisation across diverse patient populations, with the TRIPOD+AI framework recommended for transparent reporting. To mitigate bias, AI systems must undergo demographic audits, particularly in embryo selection, to prevent inequitable outcomes. Regulatory compliance with GDPR (EU), FDA (USA), and MHRA (UK) is required before clinical implementation. Transparency is critical; AI models must provide interpretable decisions, including confidence scores, feature importance, and performance metrics. Continuous post-implementation monitoring is essential to detect model drift and ensure patient safety over time. The consensus highlights that unvalidated AI models currently used in MAR clinics may introduce risks to patient outcomes. Implementing the Croatia Consensus framework will help standardise AI validation, mitigate risks, and ensure AI adoption in MAR is both evidence-based and clinically safe. Limitations, reasons for caution The consensus is based on expert opinions and current scientific literature; further empirical studies are required to validate AI best practices. The framework must evolve as AI capabilities and regulatory landscapes develop. Future research should focus on real-world AI deployment outcomes, patient safety, and long-term MAR success rates. Wider implications of the findings This is the first international AI validation framework in MAR. Standardising AI best practices will improve patient safety, optimise clinical outcomes, and enhance trust in AI-assisted fertility treatments. The framework provides a blueprint for MAR clinics, regulatory bodies, and AI developers, ensuring responsible AI integration into reproductive medicine. Trial registration number No

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

The rapid advancements in artificial intelligence (AI) have impacted various industries, including human resources (HR). This thesis aims to explore the role of AI in HR and its potential implications on organizations and employees. A comprehensive literature review was conducted to identify the various applications of AI in HR, such as recruitment, employee engagement, performance management, and training and development. The study also analyzed the potential benefits and risks associated with the integration of AI in HR, including issues related to bias, privacy, and job displacement. The findings of this study suggest that AI can enhance HR practices by improving efficiency, accuracy, and objectivity. However, the risks associated with AI adoption must be carefully considered and managed to ensure ethical and responsible use. This study provides insights into the current state of AI in HR and its future potential, offering recommendations for organizations and policymakers to maximize the benefits and minimize the risks of AI integration in the HR function. The use of artificial intelligence (AI) in human resources (HR) has become increasingly popular in recent years. AI has the potential to transform HR practices by enabling organizations to automate routine tasks, make more data-driven decisions, and improve the employee experience. However, the use of AI in HR also raises important ethical and legal considerations, such as algorithmic bias and data privacy. This thesis aims to explore the role of AI in HR and its impact on various HR functions, including recruitment and selection, employee engagement, performance management, and training and development. The study also examines the potential risks and challenges of using AI in HR and identifies strategies to mitigate these risks. The research methodology employed in this study is a mixed-methods approach, combining both qualitative and quantitative research methods. The qualitative component involves a literature review and case studies of organizations that have implemented AI in HR. The quantitative component involves a survey of HR professionals to understand their perceptions of AI in HR and their readiness to adopt AI in their organizations. The findings of this study reveal that AI has significant potential to improve HR practices, particularly in recruitment and selection, where it can reduce bias and improve the accuracy and efficiency of the hiring process. AI can also improve employee engagement by providing personalized experiences and feedback, and enhance performance management by enabling real-time monitoring and feedback. In training and development, AI can provide personalized learning experiences that meet the unique needs and preferences of individual employees. However, the study also reveals that the use of AI in HR raises important ethical and legal considerations that must be addressed. Algorithmic bias, data privacy, and the potential for job displacement are some of the key risks and challenges associated with the use of AI in HR. To mitigate these risks, organizations must adopt a proactive approach that involves regular monitoring and evaluation of AI systems, transparency in decision-making processes, and ongoing training and development for HR professionals. The study also identifies several critical success factors for the successful implementation of AI in HR, including strong leadership support, a clear understanding of business objectives, collaboration between HR and IT professionals, and a focus on employee engagement and well- being. Overall, this thesis contributes to the growing body of knowledge on the role of AI in HR and its implications for organizations and HR professionals. By identifying the potential benefits, risks, and challenges of using AI in HR, and providing strategies to mitigate these risks, this study aims to inform organizational decision-making and help HR professionals prepare for the future of work..

Taming the Complexity: Using Artificial Intelligence in a Cross-Disciplinary Innovative Platform to Redefine Molecular Imaging and Radiopharmaceutical Therapy

The integration of Artificial Intelligence (AI) into organizational processes presents unique challenges for Small and Medium‐sized Enterprises (SMEs), particularly in fostering effective human‐AI collaboration. Unlike large corporations with extensive resources for AI adoption, SMEs require adaptable frameworks tailored to their specific constraints and operational needs. This paper introduces the novel Human‐AI Collaboration Maturity Model (HAIC‐MM), which is a systems engineering framework designed to assess, guide, and enhance AI integration within SMEs. Developed through the synthesis of AI maturity models, digital transformation frameworks, and human‐machine teaming research, HAIC‐MM identifies seven dimensions and 32 capabilities across five maturity levels that are essential for successful AI adoption in SME contexts. Empirical validation through survey analysis ( N = 100) confirmed the model's robustness. Subsequent focus group analyses ( N = 10, repeated across five sessions) further validated HAIC‐MM's practical utility and alignment with the operational realities of SMEs, emphasizing its relevance to everyday challenges faced by these organizations. Pilot testing with industry practitioners ( N = 3) confirmed the usability and usefulness of the final HAIC‐MM tool. HAIC‐MM provides SME leaders with a structured, human‐centered, and systematic approach to evaluate and cultivate human‐AI collaboration, addressing key areas such as resource optimization, workforce empowerment, ethical AI oversight, and adaptive organizational culture. This research contributes to AI‐enabled systems engineering by offering a practical framework for harmonizing human and AI capabilities within resource‐constrained environments, ultimately supporting SMEs in achieving sustainable and ethically grounded AI integration across the organization. Summary This paper introduces the Human‐AI Collaboration Maturity Model (HAIC‐MM), a framework designed to address the unique AI adoption challenges faced by Small and Medium‐sized Enterprises (SMEs). The model identifies critical dimensions and capabilities needed to foster effective collaboration between humans and AI systems. The model also defines five maturity levels within each capability, allowing a granular assessment within the holistic framework. HAIC‐MM provides a practical, step‐by‐step guide to assess and enhance AI integration for SMEs. The model emphasizes ethical AI oversight, workforce empowerment, and adaptive organizational culture, while addressing key challenges like resource constraints. HAIC‐MM represents a significant contribution to the fields of systems engineering and organizational behavior, offering researchers investigating socio‐technical systems, AI integration processes, and SME innovation strategies a rigorous framework for both theoretical advancement and practical implementation. With its focus on real‐world application, HAIC‐MM equips practitioners with actionable insights to build trust, optimize collaboration between human and AI capabilities, and achieve sustainable, ethically sound AI adoption, ensuring their organizations remain competitive in an increasingly digital economy.

This research paper focuses on the role of Artificial Intelligence in UI/UX design. We know that one of the most important aspect in software development is the design of the user interface ( UI ), which refers to the look and feel of the product, and user experience ( UX ), which refers to the interaction by the user.The integration of Artificial Intelligence (AI) in User Experience (UX) and User Interface (UI) design has revolutionized digital interactions by enhancing personalization, automation, predictive analytics, and accessibility. AI-driven tools enable designers to create more intuitive, adaptive, and usercentric interfaces, improving user engagement and satisfaction. This research paper explores the various applications of AI in UX/UI, including AI-powered personalization, which tailors experiences based on user behavior, automation in design, which accelerates prototyping and layout generation, and predictive analytics, which enhances decision-making through data-driven insights. Additionally, the role of conversational AI, such as chatbots and virtual assistants, in improving user interactions is examined, along with AI's contribution to inclusive and accessible UX/UI design.Despite its advantages, the implementation of AI in UX/UI presents challenges such as data privacy concerns, ethical considerations, and potential over-reliance on automation. This paper discusses these challenges and proposes solutions to ensure that AI enhances UX/UI without compromising creativity, inclusivity, or ethical standards. The study concludes that while AI is transforming UX/UI design, a balanced approach combining AI-driven efficiency with human creativity is essential for building truly user-friendly and ethical digital experiences.