2026 Healthcare Predictions: AI, Blockchain, and the Rise of Decentralized Innovation.

Artificial Intelligence has quietly worked its way into everyday aesthetic practice in India. What began as a novelty mostly confined to skin scanners and consumer apps- has now spread into diagnosis, treatment planning, procedural safety, patient counselling, and even clinic operations. This shift carries particular weight in the Indian setting, where Fitzpatrick skin types III?VI dominate, pigmentary disorders remain difficult to judge by eye alone, and patients increasingly ask for numbers, visuals, and forecasts rather than opinions. This paper looks closely at how AI is being used across Indian aesthetic medicine today, covering skin and hair diagnostics, laser and energy-based treatments, injectables, three-dimensional simulation, robotics, and clinic management systems. It also examines how clinics and organized chains are weaving AI into their workflows to stay competitive, often using it as a signal of credibility rather than spectacle. Drawing on Indian industry coverage and market activity, the paper traces why adoption has accelerated so quickly, what clinics gain by moving early, and what risks emerge when adoption is delayed. It closes by mapping how AI usage is likely to unfold over the next twelve months, arguing that AI has shifted from optional add-on to structural backbone for aesthetic practice in India.

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.

Focus issue: Artificial intelligence in medical physics.

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

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Recent scholarship and expert commentary emphasize the transformative yet precarious role of artificial intelligence (AI) in education. Studies highlight AI’s potential to personalize learning, enhance engagement, and optimize institutional operations, while underscoring the importance of ethical design, student motivation, and faculty readiness. Frameworks integrating AI into curricula stress the need for digital literacy, inclusive governance, and responsible innovation. However, risks—from academic dishonesty to existential threats posed by Artificial General Intelligence (AGI)—require urgent attention. Eric Schmidt’s warning about AI’s unpredictable autonomy, particularly in military systems, echoes calls for global safety standards, oversight, and a moratorium on large training runs. A comprehensive, multidimensional approach—including international cooperation, ethical frameworks, and public engagement—is essential to mitigate AGI risks. As AI evolves, educational institutions must balance innovation with accountability, ensuring that AI enhances learning and aligns with societal values and safeguards against catastrophic outcomes. Human oversight remains paramount in this emerging landscape. The pivotal question is not “how” to use AI, but whether it should be used at all!

The brave new world of artificial intelligence: dawn of a new era

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

Artificial Intelligence (AI) is transforming industries and reshaping human-machine interactions at an unprecedented pace. From healthcare and finance to autonomous systems and creative fields, AI is driving innovation across multiple domains. This paper explores key emerging trends that will define the future of AI, including explainable AI (XAI) for greater transparency, the integration of quantum computing processing power, advancements in autonomous systems such as self-driving vehicles and drones, and the role of AI in content generation and creativity. Additionally, AI is playing a crucial role in addressing global challenges, such as climate change and sustainable resource management, while also contributing to personalized healthcare and human augmentation. Despite its potential, AI presents significant ethical and societal challenges. Issues such as algorithmic bias, data privacy, and job market disruptions raise concerns about fairness and accountability. The future of AI holds immense promise, but responsible innovation will be key to maximizing its benefits while mitigating risks. KEYWORDS: Artificial Intelligence, Artificial General Intelligence, Natural Language Processing, AI Ethics

Artificial Intelligence (AI) is revolutionizing the field of health sciences, reshaping how we teach, learn, and practice medicine. As AI technologies become increasingly integrated into healthcare systems, their impact on health sciences education cannot be overstated. From personalized learning experiences to advanced diagnostic training, AI is poised to enhance the quality and accessibility of education for future healthcare professionals. However, this transformation also raises critical questions about ethics, equity, and the future role of educators in an AI-driven world. The transformative role of Artificial Intelligence (AI) in health sciences education is increasingly recognized as a pivotal factor in shaping the future of medical training and practice. As AI technologies continue to evolve, their integration into educational curricula presents both opportunities and challenges that must be carefully navigated to enhance the learning experience for future healthcare professionals. One of the most significant contributions of AI to health sciences education is its ability to personalize learning. Traditional teaching methods often follow a one-size-fits-all approach, which can leave some students struggling to keep up while others are not sufficiently challenged. AI-powered platforms, such as adaptive learning systems, analyze individual student performance and tailor content to meet their unique needs. For example, tools like Osmosis and AMBOSS use AI to provide customized study plans, ensuring that students focus on areas where they need the most improvement (Topol, 2019). This personalized approach not only improves learning outcomes but also fosters a more inclusive educational environment. AI is also transforming clinical training by simulating real-world scenarios. Virtual patient simulations, powered by AI, allow students to practice diagnosing and treating conditions in a risk-free environment. These simulations can replicate rare or complex cases that students might not encounter during their clinical rotations. For instance, platforms like Touch Surgery and SimX use AI to create immersive surgical and emergency care simulations, providing students with hands-on experience before they enter the operating room (McGaghie et al., 2011). Such tools bridge the gap between theory and practice, preparing students for the complexities of modern healthcare. Moreover, AI is enhancing the role of educators by automating administrative tasks and providing data-driven insights into student performance. Grading, attendance tracking, and even curriculum design can be streamlined using AI, allowing educators to focus on mentoring and engaging with students. AI-driven analytics can also identify at-risk students early, enabling timely interventions to support their academic success (Wartman & Combs, 2018). By augmenting the capabilities of educators, AI empowers them to deliver more impactful and student-centered teaching. AI's potential to revolutionize health sciences education lies in its ability to personalize learning experiences and improve educational outcomes. For instance, AI-driven tools can facilitate realistic simulations and automated assessments, allowing students to engage in practical scenarios that mimic real-world clinical situations (Santos & Lopes, 2024). This capability not only enhances the learning process but also prepares students for the complexities of patient care in a technology-driven environment (Grunhut et al., 2022). Furthermore, the incorporation of AI into curricula can foster critical thinking and decision-making skills, essential for navigating the ethical dilemmas that arise in medical practice (Grunhut et al., 2022). Despite the promising applications of AI in education, the integration of these technologies into medical curricula has been slow. A scoping review highlighted that many medical schools have yet to adopt AI training, primarily due to a lack of systematic evidence supporting its implementation (Lee et al., 2021). Additionally, concerns regarding data protection and the ethical implications of AI use in healthcare education have been raised, indicating a need for comprehensive AI education that addresses these issues (Veras et al., 2023; Frehywot & Vovides, 2023). Students have expressed a desire for more robust training in AI, emphasizing the importance of understanding its role in healthcare delivery and decision-making processes (Ahmad et al., 2023; Derakhshanian et al., 2024). Moreover, the rapid advancement of AI technologies necessitates continuous curriculum updates to keep pace with emerging trends. As noted in recent literature, the integration of AI into biomedical science curricula should include subjects related to informatics, data sciences, and digital health (Sharma et al., 2024). This approach not only equips students with the necessary skills to utilize AI effectively but also prepares them for the evolving landscape of healthcare, where AI will play an integral role in diagnostics, treatment personalization, and patient management (Santos & Lopes, 2024; Secinaro et al., 2021). However, the implementation of AI in health sciences education is not without challenges. Ethical considerations surrounding AI's impact on healthcare equity and the potential for bias in AI algorithms must be addressed (Frehywot & Vovides, 2023; Han et al., 2019). Ensuring that AI technologies are used responsibly and equitably in education and practice is crucial to avoid exacerbating existing disparities in healthcare access and outcomes (Rigby, 2019). Furthermore, the lack of faculty expertise in AI poses a significant barrier to its integration into medical education, highlighting the need for targeted training and resources for educators (Derakhshanian et al., 2024). However, the integration of AI into health sciences education is not without challenges. Ethical concerns, such as data privacy and algorithmic bias, must be addressed to ensure that AI tools are used responsibly. Additionally, there is a risk of over-reliance on AI, potentially undermining the development of critical thinking and clinical judgment skills. Educators must strike a balance between leveraging AI’s capabilities and preserving the human elements of teaching and learning. Equity is another pressing issue. While AI has the potential to democratize education, access to these technologies remains uneven. Institutions in low-resource settings may struggle to adopt AI-driven tools, exacerbating existing disparities in global health education. Policymakers and educators must work together to ensure that the benefits of AI are accessible to all, regardless of geographic or socioeconomic barriers. In conclusion, AI is a powerful tool that holds immense promise for transforming health sciences education. By personalizing learning, enhancing clinical training, and supporting educators, AI can help prepare the next generation of healthcare professionals to meet the demands of an increasingly complex healthcare landscape. However, its integration must be guided by ethical principles and a commitment to equity, However, the successful integration of AI into educational curricula requires a concerted effort to address ethical concerns, update training programs, and equip both students and faculty with the necessary knowledge and skills. As the healthcare landscape continues to evolve, embracing AI in education will be essential for fostering a new generation of healthcare providers who are adept at leveraging technology to improve patient care. As we embrace this technological revolution, we must remember that AI is not a replacement for human expertise but a complement to it. The future of health sciences education lies in the synergy between human ingenuity and artificial intelligence.

Artificial intelligence in medical practice: current status and future perspectives

Introduction Health systems face escalating demands from growing population needs amidst workforce shortages of 1.2 million doctors, nurses and midwives. Artificial intelligence (AI) has emerged as a pivotal tool in transforming primary care. By aiding in clinical decision-making, automating routine tasks, and predicting patient needs, AI can bolster the efforts of health professionals and enhance patient outcomes. This workshop seeks to examine AI applications within primary care and explore their implications for healthcare providers, focusing on their integration strategies and addressing their impact on the skills of health professionals. Particular emphasis will be placed on AI applications managing non-communicable diseases such as cancer, cardiovascular diseases, chronic respiratory diseases and diabetes, which represent significant health burdens. Methods The workshop will feature concise pitch presentations by leading AI experts and healthcare practitioners from across Europe, highlighting AI technologies and their potential applications in primary care, with a focus on managing non-communicable diseases. These presentations and the following engaging panel discussion will showcase successful AI implementations, their practical applications in primary care settings and expected benefits for patient outcomes. The discussion will also consider the skills that healthcare professionals will need as AI becomes more integrated into their work, discussing strategies for continuous education and training to adapt to these technological advancements. In an interactive format, attendees will propose questions and identify valuable AI integration areas, with audience input guiding the discussion and encouraging a critical examination of AI's role in primary care. This will be done through live questions, voting on dilemmas, submitting real-life cases or proposing “what if” scenarios. Discussion and Conclusions This workshop will provide participants with insights into AI's transformative potential in primary care, particularly for managing non-communicable diseases. Attendees will gain insights from practitioners who have successfully integrated AI solutions, understanding the benefits and practical strategies for AI implementation, focusing on a human-centered approach and responsible innovation. With continuous education and training emphasised, participants will be equipped with knowledge to leverage AI effectively, enhancing workforce efficiency. By integrating diverse European experiences, the workshop will enrich the discussion, providing a comprehensive understanding and actionable strategies for AI integration in primary care systems. By facilitating a broader adoption of AI technologies, healthcare professionals can help transform care delivery, ensuring that primary care systems are resilient, efficient, and equipped to meet the needs of future patient populations such as the high burden of non-communicable diseases. Key messages • AI empowers primary care through task automation, risk prediction and decision support in prevention, diagnosis and treatment, crucial for managing growing patient needs and workforce challenges. • Emerging AI applications can provide key insights on the required skills development needs and integration strategies of AI in primary care to address non-communicable diseases. Speakers/Panellists Tino Marti Department of Health. Government of Catalonia, Barcelon, Spain Artin Entezarjou Tandem Health, Stockholm, Sweden Gregor Stiglic University of Maribor, Maribor, Slovenia Eric Sutherland OECD, Paris, France Federicaa Margheri European Health Management Association, Brussels, Belgium

What drives people to have trust in using artificial intelligence (AI)? How does the institutional environment shape social trust in AI? This study addresses these questions to explain the role of institutions in allowing AI‐based technologies to be socially accepted. In this study, social trust in AI is situated in three institutional entities, namely, the government, tech companies, and the scientific community. It is posited that the level of social trust in AI is correlated to the level of trust in these institutions. The stronger the trust in the institutions, the deeper the social trust in the use of AI. To test this hypothesis, we conducted a cross‐country survey involving a total of 4037 respondents in Singapore, Taiwan, Japan, and the Republic of Korea (ROK). The results show convincing evidence of how institutions shape social trust in AI and its acceptance. Our empirical findings reveal that trust in institutions is positively associated with trust in AI technologies. Trust in institutions is based on perceived competence, benevolence, and integrity. It can directly affect people’s trust in AI technologies. Also, our empirical findings confirm that trust in AI technologies is positively associated with the intention to use these technologies. This means that a higher level of trust in AI technologies leads to a higher level of intention to use these technologies. In conclusion, institutions greatly matter in the construction and production of social trust in AI‐based technologies. Trust in AI is not a direct affair between the user and the product, but it is mediated by the whole institutional setting. This has profound implications on the governance of AI in society. By taking into account institutional factors in the planning and implementation of AI regulations, we can be assured that social trust in AI is sufficiently founded.

The integration of artificial intelligence (AI) into healthcare is accelerating, with dermatology standing out as a key field where AI's potential is evident. In 2025, Ireland seeks to implement Sláintecare and transform its health and social care system by establishing a universal, single-tier, person-centred service. Sláintecare declares its goal to be ‘improving health and social care services in Ireland, to optimise patient outcomes and be responsive to their needs’. With such ambitious goals, one obvious aid could be the embracing of AI in medicine and, in particular, the dermatology sector. AI, particularly deep learning algorithms, has already demonstrated impressive diagnostic accuracy in dermatology, particularly for identifying skin cancers like melanoma. Studies indicate that AI can match, and in some cases surpass, experienced clinicians in diagnostic performance [1, 2]. Yet, despite its potential, AI is not yet a regular tool in Irish dermatology departments. Ireland's first rapid access teledermatology service Dermview was founded in 2018, in the hope of reducing waiting times and increase access. However, many clinics face challenges in adapting to this model. Telemedicine has been met with scepticism by dermatologists in the assessment of conditions such as the diagnosis of pigmented lesions [3]. Dermatology in Ireland faces a substantial patient backlog. At the time of writing, there are over 50,000 people currently waiting for an appointment [4, 5]. The number of dermatologists per 100,000 people is one of the lowest in Europe, further exacerbating this issue [4]. AI could be instrumental in alleviating some of these pressures, such as by triaging referrals, assisting with mole mapping or identifying high-priority lesions. This would not only reduce waiting times but also improve care for those in underserved rural areas [6]. Despite AI's demonstrated successes, its widespread adoption faces several hurdles. One of the major challenges is clinician scepticism. A global survey revealed that 22% of dermatologists are unsure about AI's reliability and its role in patient management [7]. This scepticism is compounded by the emotional difficulty of accepting that a machine, with only a few years of existence, might outperform a clinician with decades of experience. However, AI's achievements in other domains, such as radiological imaging, clearly highlight its capability to learn and adapt. Another challenge is patient trust, which is likely to be earned, not given. Studies show that while patients tend to trust dermatologists over AI, their attitudes towards AI improve as they see its benefits. Trust can be fostered through transparent communication and patient engagement. Over time, as AI demonstrates its efficacy in clinical settings, both clinicians and patients may become more accepting of its role in diagnosis and management [8]. AI in dermatology is already showing promise globally. For instance, convolutional neural networks (CNNs) are highly effective in detecting skin cancer, and teledermatology platforms like DermView have enhanced remote care. Additionally, predictive analytics powered by AI could optimise treatment pathways and reduce unnecessary wait times [1]. Ireland's growing health tech sector, abundance of multinational tech corporations such as Google and Meta, combined with its highly educated workforce and strong academic institutions provide an opportunity to lead the way in AI adoption. However, the country must address several issues: ensuring that training datasets reflect its increasingly diverse population, navigating regulatory challenges and overcoming clinician resistance. Establishing national databases for AI model development during the reform of its healthcare service, launching pilot programs and incorporating AI into dermatology training programs could further streamline the process. The integration of AI into dermatology in Ireland could also contribute to a wider momentum shift across Europe. As health systems across the EU struggle with rising demand, workforce shortages and increasing expectations for equitable access, successful implementation in one country can serve as a catalyst for regional adoption. One paper notes regional differences within Europe, with European respondents expressing both optimism and reservations about AI integration, especially regarding diagnostic reliability and job security [9]. However, should Ireland be successful in adopting AI within dermatology as well as improve its ability to use electronic medical records (EMR), we could potentially pave the way for effective, efficient data sharing with benefits for both patients, doctors and researchers. Thus, a coordinated, Europe-wide approach could therefore accelerate both innovation and trust in AI-driven dermatology. AI offers transformative potential for dermatology in Ireland and potentially many countries within Europe, by potentially improving diagnostic accuracy and reducing patient waiting times. With strategic investment, enhanced education and collaboration with international tech companies, Ireland has the opportunity to lead the charge in AI-driven dermatology and could serve as the spark that ignites broader transformation across Europe. The author has drafted the manuscript and critically reviewed its content autonomously. The author has nothing to report. The author declares no conflicts of interest. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.