Can an Artificial Intelligence (AI) Be an Author on a Medical Paper?

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

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

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

From black box to clarity: Strategies for effective AI informed consent in healthcare.

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

With the rapid advancement of artificial intelligence (AI) technologies and their implementation in health care, there is a critical need to explore the different applications and implications of AI. This workshop seeks to look at different aspects of AI, both potential applications for AI technology in health, public opinions of use of AI in health care, and the reality of implementing AI in different contexts. Understanding health care approaches to AI from different countries is a unique opportunity for shared learning of innovations across various sectors of health care and patient engagement in order to most effectively engage this emerging field. AI, in whichever form it may be incorporated into health care, has the opportunity to impart meaningful, innovative change, but understanding this new technology comes with important questions. This session will dive into research on AI in various areas of health care by 1) looking at different ways AI is being harnessed in health care; 2) discussing lessons which can be learned and adapted from other countries; 3) discussing promising solutions and approaches to implementing AI in health care; 4) discussing the limitations or concerns of integrating AI in health care. The panel will be composed of 1 moderator and 2 panelists. Panelists are experts in their fields, established researchers and practitioners, and alumni of the Harkness Fellowship from Germany and the United Kingdom. A moderator will briefly frame the discussion for the audience and introduce the speakers and their presentations before leading an interactive, engaging question and answer portion. The coherence of this panel lays in its theme of leveraging AI to enhance healthcare delivery and understanding ways we can work across countries to foster ethical, equitable AI technologies that revolutionize healthcare practices with the ultimate goal of improving patient outcomes and health system efficiencies. Dr. Saira Ghafur from the U.K. will begin by presenting her research on evidence-based reasons for implementing AI in health care and Dr. Benedikt Simon from Germany will close out by discussing his work in AI in Germany and the challenges that have prevented robust integration. Each panelist will speak for roughly 8-10 minutes, with about 5 minutes reserved for moderator and audience questions. Key messages • Sharing cross-country experiences and research enables us to better understand how AI can impact health care. • In weighing both the benefits and challenges or limitations of implementing AI, we can best understand effective approaches to utilizing AI in health care.

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

The application of generative artificial intelligence (AI) technology in the healthcare sector can significantly enhance the efficiency of China’s healthcare services. However, risks persist in terms of accuracy, transparency, data privacy, ethics, and bias. These risks are manifested in three key areas: first, the potential erosion of human agency; second, issues of fairness and justice; and third, questions of liability and responsibility. This study reviews and analyzes the legal and regulatory frameworks established in China for the application of generative AI in healthcare, as well as relevant academic literature. Our research findings indicate that while China is actively constructing an ethical and legal governance framework in this field, the regulatory system remains inadequate and faces numerous challenges. These challenges include lagging regulatory rules; an unclear legal status of AI in laws such as the Civil Code; immature standards and regulatory schemes for medical AI training data; and the lack of a coordinated regulatory mechanism among different government departments. In response, this study attempts to establish a governance framework for generative AI in the medical field in China from both legal and ethical perspectives, yielding relevant research findings. Given the latest developments in generative AI in China, it is necessary to address the challenges of its application in the medical field from both ethical and legal perspectives. This includes enhancing algorithm transparency, standardizing medical data management, and promoting AI legislation. As AI technology continues to evolve, more diverse technical models will emerge in the future. This study also proposes that to address potential risks associated with medical AI, efforts should be made to establish a global AI ethics review committee to promote the formation of internationally unified ethical and legal review mechanisms.

Artificial Intelligence (AI) technologies are expected to solve pressing challenges in healthcare services worldwide. However, the current state of introducing AI is characterised by several issues complicating and delaying their deployments. These issues concern topics such as ethics, regulations, data access, human trust, and limited evidence of AI technologies in real-world clinical settings. They further encompass uncertainties, for instance, whether AI technologies will ensure equal and safe patient treatment or whether the AI results will be accurate and transparent enough to establish user trust. Collective efforts by actors from different backgrounds and affiliations are required to navigate this complex landscape. This article explores the role of such collective efforts by investigating how an informally established network of professionals works to enable AI in the Norwegian public healthcare services. The study takes a qualitative longitudinal case study approach and is based on data from non-participant observations of digital meetings and interviews. The data are analysed by drawing on perspectives and concepts from Science and Technology Studies (STS) dealing with innovation and sociotechnical change, where collective efforts are conceptualised as actor mobilisation. The study finds that in the case of the ambiguous sociotechnical phenomenon of AI, some of the uncertainties related to the introduction of AI in healthcare may be reduced as more and more deployments occur, while others will prevail or emerge. Mobilising spokespersons representing actors not yet a part of the discussions, such as AI users or researchers studying AI technologies in use, can enable a ‘stronger’ hybrid knowledge production. This hybrid knowledge is essential to identify, mitigate and monitor existing and emerging uncertainties, thereby ensuring sustainable AI deployments.

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

Purpose This paper aims to explore the paradigm shift in leadership and strategic management driven by the integration of responsible artificial intelligence (AI) in healthcare. It explores the evolving role of leadership in adapting to AI technologies while ensuring ethical governance, transparency and accountability in healthcare decision-making. Design/methodology/approach This study conducts a comprehensive review of current literature, case studies and industry reports to evaluate the implications of responsible AI adoption in healthcare leadership. It focuses on key areas such as AI-driven decision-making, resource optimisation, crisis management and patient care, while also addressing challenges in integrating AI technologies effectively. Findings The integration of AI in healthcare is transforming leadership from traditional, experience-based decision-making to data-driven, AI-enhanced strategies. Responsible leadership emphasises addressing ethical concerns such as bias, transparency and accountability. AI technologies improve resource allocation, crisis management and patient care, but challenges such as workforce resistance and the need for upskilling healthcare professionals remain. Practical implications Healthcare leaders must adopt a responsible leadership framework that balances AI’s potential with ethical and human-centred care principles. Recommendations include developing AI literacy programmes for healthcare professionals, ensuring inclusivity in AI algorithms and establishing governance policies that promote transparency and accountability in AI applications. Originality/value This paper provides a critical, forward-looking perspective on how responsible AI can drive a paradigm shift in healthcare leadership. It offers novel insights into the integration of AI within healthcare organisations, emphasising the need for leadership that prioritises ethical AI usage and promotes patient well-being in a rapidly evolving digital landscape.

As artificial intelligence (AI) is becoming more integrated into the field of healthcare, medical students need to learn foundational AI literacy. Yet, traditional, descriptive teaching methods of AI topics are often ineffective in engaging the learners. This article introduces a new application of cinema to teaching AI concepts in medical education. With meticulously chosen movie clips from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie, the students were introduced to the primary differences between artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI). This method triggered encouraging responses from students, with learners indicating greater conceptual clarity and heightened interest. Film as an emotive and visual medium not only makes difficult concepts easy to understand but also encourages curiosity, ethical consideration, and higher order thought. This pedagogic intervention demonstrates how narrative-based learning can make abstract AI systems more relatable and clinically relevant for future physicians. Beyond technical content, the method can offer opportunities to cultivate critical engagement with ethical and practical dimensions of AI in healthcare. Integrating film into AI instruction could bridge the gap between theoretical knowledge and clinical application, offering a compelling pathway to enrich medical education in a rapidly evolving digital age.NEW & NOTEWORTHY This article introduces a new learning strategy that employs film to instruct artificial intelligence (AI) principles in medical education. By introducing clips the from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie to clarify artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI), the approach converted passive learning into an emotionally evocative and intellectually stimulating experience. Students experienced enhanced comprehension and increased interest in artificial intelligence. This narrative-driven, visually oriented process promises to incorporate technical and ethical AI literacy into medical curricula with enduring relevance and impact.

Impact of Artificial Intelligence on Clinical Decision-Making in Health Care

Building Trust: Developing an Ethical Communication Framework for Navigating Artificial Intelligence Discussions and Addressing Potential Patient Concerns

In recent years, the rapid development of AI technology Generative AI, has restructured the healthcare industry. Generative AI is a collection of algorithms that uses a large volume of medical data to generate new data in various formats, including medical images, data augmentation, and medicine development. A variety of techniques are employed in Generative AI in the healthcare industry, which includes Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), AutoRegressive Models, Flow-Based Models, and Probabilistic Graphical Models. Generative AI can applied in various domains in the healthcare sector including drug discovery, medical imaging enhancement, data augmentation, anomaly detection, simulation and training, and predictive modelling. The integration of Generative AI faces some challenges, such as addressing ethical and legal issues related to the use of Artificial Intelligence (AI) in healthcare and synthetic data in clinical decision-making, and ensuring the reliability and interpretability of AI-generated outputs.