Radiology departments today are confronting an unprecedented surge in imaging volume, complexity, and data throughput. Traditional manual review methods struggle to keep pace, leading to bottlenecks that threaten timely diagnosis and patient care. AI, primarily driven by advances in deep learning and computer vision algorithms, offers a promising solution by automating routine tasks, enhancing image analysis accuracy, and expediting report generation. However, the successful integration of such sophisticated technologies into everyday workflows requires far more than algorithmic innovation. It demands comprehensive infrastructural upgrades and systemic recalibration.

One core message articulated by Gelareh Sadigh, MD, associate editor for health services research at JACR, is that AI must be embedded seamlessly into the clinical workflow to realize its benefits. The mere presence of intelligent algorithms is insufficient if these systems cannot communicate efficiently with existing PACS (Picture Archiving and Communication Systems), RIS (Radiology Information Systems), and EMR (Electronic Medical Records) platforms. Moreover, institutional policies, regulatory frameworks, and reimbursement models currently lag behind rapid AI development, representing significant obstacles to full-scale deployment.

Poorly integrated AI applications risk not only inefficiency but also deterioration of workflow quality and clinician satisfaction. There are concerns that misaligned AI tools could increase cognitive burden by introducing non-intuitive interfaces or overwhelming practitioners with false positives. Additionally, unvetted AI systems may inadvertently perpetuate biases present in training datasets, potentially exacerbating health disparities. Addressing these multidimensional challenges is essential to harness AI’s transformative potential without compromising patient safety or equity.

The articles comprising this JACR focus issue collectively signal a paradigm shift in radiology. Workflow optimization is recognized not as a peripheral advantage, but as the decisive metric for AI’s success. Practical usability, ease of integration, and demonstrable improvements in care delivery will ultimately determine which AI tools gain traction. This perspective recalibrates the criteria for success away from isolated performance metrics toward holistic clinical value.

From an engineering standpoint, the development of AI-enabled intelligent workflows involves multifaceted system design encompassing data ingestion pipelines, real-time image processing, contextual decision support, and adaptive user interfaces. These workflows leverage convolutional neural networks (CNNs) for pattern recognition, natural language processing (NLP) for automated report generation, and reinforcement learning for optimizing task sequencing. Such integrated approaches aspire to shift radiology from reactive interpretation toward proactive, data-driven clinical pathways.

Another pivotal consideration lies in standardizing performance evaluation protocols tailored to workflow impacts rather than solely diagnostic accuracy. This demands multidisciplinary collaborations bridging radiologists, data scientists, informatics specialists, and healthcare administrators. Longitudinal studies assessing key indicators such as report turnaround times, diagnostic confidence, patient outcomes, and clinician satisfaction will be vital to quantifying AI’s true clinical utility.

Further complicating this landscape is the imperative to ensure robust data security and patient privacy in AI workflows, particularly given the sensitive nature of medical imaging data. Federated learning frameworks and differential privacy techniques present promising avenues to enable collaborative model training without compromising confidentiality. Such innovations will be instrumental in scaling AI adoption across diverse institutions while adhering to stringent regulatory standards.

Importantly, AI-driven workflow optimization holds potential far beyond volumetric image interpretation. Intelligent algorithms can assist in prognostication, triaging urgent cases, integrating multimodal data from genomics and clinical parameters, and facilitating personalized medicine approaches. This broader vision situates radiology at the nexus of precision healthcare, empowered by continuous learning systems that evolve with accumulating clinical experience.

Despite these advantages, the transition to AI-augmented radiology workflows is not without risk. Institutions must cultivate a culture of ongoing validation and human oversight, ensuring that AI serves as a trusted adjunct rather than an opaque black box. Education and training for radiologists on AI literacy are critical to empowering clinicians to effectively supervise algorithmic outputs and intervene when necessary.

The editorial leadership at JACR, epitomized by Editor-in-Chief Ruth C. Carlos, MD, MS, emphasizes the urgency of navigating this rapidly shifting landscape with evidence-based signposts. Radiology is at a crossroads where thoughtful implementation of AI can either propel the field forward or introduce unintended disruptions. Strategic investments in technological infrastructure, regulatory alignment, and multidisciplinary collaboration are key levers to shape this future beneficially.

In summary, the JACR’s focus issue offers a timely and profound exploration of AI’s role in evolving radiological workflows. The transformation from image overload to intelligent workflows encapsulates a broader digital revolution in healthcare, wherein AI tools must be pragmatically integrated to enhance efficiency, accuracy, and equity. Radiologists, institutions, and policymakers have a shared responsibility to ensure that the promise of AI translates into measurable clinical improvements and sustainable changes in practice.

This pivotal collection serves as a clarion call for the radiology community to view AI not simply as a technological novelty but as a foundational component of future diagnostic paradigms. As the volume and complexity of imaging data continue to escalate exponentially, the development and implementation of intelligent workflows will be essential to maintain the high standards of patient care and safety that modern healthcare demands.

Subject of Research: Not applicable
Article Title: From Image Overload to Intelligent Workflows
News Publication Date: 3-Mar-2026
Web References: http://dx.doi.org/10.1016/j.jacr.2026.01.001

Keywords

Radiology, Artificial intelligence, Medical journals, Health care, Health care delivery, Clinical imaging, Diagnostic imaging

At the heart of this study is the acknowledgment that generative AI can enhance nursing practice in several ways. From automating administrative tasks to providing clinical decision support, AI can alleviate some of the burdens that nurses face daily. The researchers sought to identify the factors that motivate or inhibit nurses’ willingness to embrace these new technologies, contributing to a more nuanced understanding of the human-AI interface in healthcare.

As generative AI continues to make significant strides, the nursing profession finds itself at a crossroads. On one hand, nurses are excited about the potential improvements in their workflow; on the other, concerns about job displacement and ethical implications loom large. The findings of the study suggest that intrinsic motivation, training, and organizational support play crucial roles in shaping nurses’ attitudes toward AI integration. The research highlights that without addressing these factors, the widespread adoption of generative AI in nursing may face formidable obstacles.

The study surveyed a diverse group of nurses across various specialties and experience levels. By using a well-defined set of metrics to assess the factors associated with their intention to use generative AI, the research provides valuable insights into the perceptions of nurses in relation to technology within healthcare settings. One significant finding is that familiarity with AI systems correlated positively with a willingness to engage with these technologies. This underscores the necessity for comprehensive training programs that enhance nurses’ understanding and skills related to generative AI.

Moreover, the study identified that organizational culture plays an influential role in the acceptance and integration of AI in nursing practices. Environments that foster collaboration, innovation, and continuous learning are more likely to see enthusiastic adoption of these advanced technologies among their nursing staff. Organizations that prioritize technology adoption must also create robust communication strategies to clarify the benefits and usage of AI tools. Addressing concerns and encouraging dialogue can build a more favorable climate for embracing AI.

Another striking outcome of the research revealed that educational background and ongoing professional development significantly impact nurses’ intention to utilize generative AI. Those with higher levels of education expressed a greater inclination toward adopting new technologies, indicating that advanced training could be instrumental in shaping a skilled workforce prepared to leverage AI efficiently. Integrating AI into nursing curricula is a proactive step toward ensuring that upcoming generations of nurses are well-equipped to thrive in a tech-savvy healthcare landscape.

The study also spotlighted the psychological aspects influencing nurses’ perceptions of AI. Fear of job loss and doubts about AI’s reliability can deter nursing staff from utilizing generative AI. This highlights the importance of demonstrating the complementary nature of AI tools, wherein technology enhances nursing capabilities rather than replacing human expertise. Developing trust in AI as a partner in patient care will require consistent, positive experiences and transparent outcomes showcasing AI’s effectiveness.

Choe and Woo’s study emphasizes the necessity of a multi-faceted approach to encourage acceptance of generative AI among nurses. This includes engaging with nurses through feedback loops to understand their concerns and incorporating their input in the development of AI tools tailored to their needs and workflows. When nurses feel heard and involved in the process, their acceptance and willingness to embrace the changes brought by AI technologies significantly improve.

As the study indicates, the timeline for widespread implementation of generative AI in nursing is contingent upon various factors. Organizational readiness, the political landscape, and regulatory frameworks will all contribute to how swiftly these technologies can be integrated into practice. Policymakers play a critical role in promoting standards and guidelines to ensure that AI’s usage in healthcare is safe, ethical, and beneficial for both practitioners and patients.

In conclusion, Choe and Woo’s study sheds light on the pivotal factors influencing the willingness of nurses to adopt generative AI technologies. It presents a roadmap for overcoming barriers through targeted training, fostering a supportive organizational culture, and addressing ethical considerations. The healthcare industry stands at the brink of a revolution, with opportunities to transform nursing practice through innovative technology. By prioritizing education and collaboration, the nursing profession can ensure that it advances alongside technological developments, ultimately leading to enhanced patient care and efficient workflow management.

In an ever-changing healthcare environment, the focus on the human element amid technological innovations remains crucial. As generative AI becomes increasingly prevalent, understanding the perspectives of healthcare professionals will provide invaluable insights into creating a brighter future for both nursing practice and patient outcomes.

Subject of Research: Factors associated with intention to use generative artificial intelligence in nursing practice.

Article Title: Factors associated with intention to use generative artificial intelligence in nursing practice: a cross-sectional study.

Article References:

Choe, J., Woo, K. Factors associated with intention to use generative artificial intelligence in nursing practice: a cross-sectional study.BMC Nurs 24, 1327 (2025). https://doi.org/10.1186/s12912-025-03985-y

Image Credits: AI Generated

DOI: 10.1186/s12912-025-03985-y

Keywords: Generative AI, nursing practice, healthcare technology, perception of AI, organizational culture, education in nursing, adoption barriers, nursing workforce.

Healthcare administrators today face an unprecedented level of ambiguity as AI technologies permeate fundamental aspects of their operations, including reimbursement schemes, clinical documentation workflows, workforce planning, and vendor ecosystems. Increasingly, predictive AI tools are integrated into insurance decision-making, raising critical issues regarding the balance between clinical judgment and financially driven algorithms. These technologies not only automate procedures but also embed complex financial calculations, creating tension between efficacy, transparency, and ethical care provision.

The transition of electronic health records (EHR) systems to cloud-based infrastructures exemplifies another domain where AI-driven complexity is reframing healthcare IT strategies. Hospitals report enhanced scalability and system reliability when migrating to public cloud services, yet they simultaneously grapple with a shortage of specialized cloud expertise. The dependence on third-party consultants reflects an ongoing talent realignment challenge, as internal capabilities lag behind the accelerating pace of technological change, raising governance and strategic coordination dilemmas at the executive level.

Furthermore, the ambient clinical documentation market is evolving rapidly toward commoditization, dominated by a few influential players such as Epic and Microsoft. These incumbents leverage platform moats, creating high barriers to entry for smaller AI startups eager to innovate within healthcare infrastructure. This consolidation underlines the critical need to understand vendor dynamics and their strategic implications for digital health governance, competition, and innovation sustainability.

Effective leadership in this context requires sophisticated governance frameworks capable of adapting dynamically to shifting regulatory landscapes, technological advancements, and stakeholder expectations. A recent scoping review published within the Journal of Medical Internet Research highlighted that successful AI adoption depends heavily on multidimensional leadership—combining technological acumen, strategic foresight, and empathetic stakeholder engagement. These findings emphasize the importance of equipping healthcare executives with practical, evidence-driven strategies to manage AI’s inherent uncertainties.

The call for papers is thus positioned to gather empirical and case-study evidence that elucidates pathways for navigating these uncertainties. Submissions are encouraged to furnish actionable insights for senior management and administrators on leveraging AI tools to foster value-based, equitable healthcare. By facilitating this knowledge exchange, the journal aims to foster a comprehensive understanding of AI’s strategic implications and build a cohesive knowledge base that informs policy and practice alike.

Among the critical thematic areas for exploration are AI’s influence on reimbursement frameworks, where the substitution of clinical judgment with algorithmic decision-making engenders financial and ethical uncertainty. Researchers are invited to examine how AI-enabled reimbursement models reshape risk assessments, cost management, and payer-provider negotiations in ways that impact patient outcomes and organizational sustainability.

Cloud migration challenges also beckon rigorous inquiry. Investigating how health institutions realign talent and develop governance protocols during and after transitions to AI-enhanced cloud environments will provide valuable strategic lessons. Understanding how to balance internal capacity-building with external expertise is particularly significant in ensuring continuity, security, and compliance amid rapid digital transformation.

The evolving vendor landscape constitutes another vital dimension, where power disparities affect innovation trajectories and healthcare delivery models. Detailed explorations of vendor collaborations, competitive strategies, and their implications for AI accessibility and interoperability within healthcare ecosystems will enrich discourse on fostering inclusive and resilient digital health infrastructures.

Strategic decision-making under AI-enabled uncertainty remains a core concern, as administrators navigate complex trade-offs involving technological risks, ethical considerations, operational disruptions, and regulatory compliance. Investigations into decision frameworks, risk assessment methodologies, and adaptive leadership approaches will illuminate pathways for enhancing organizational agility and effectiveness.

Moreover, the emergence of learning health systems in the AI era offers a promising paradigm shift—integrating continuous data feedback loops, real-time analytics, and adaptive policy frameworks to drive evidence-based improvements. Studies articulating how AI accelerates learning health system capabilities while managing attendant uncertainties will be particularly impactful.

JMIR Publications, known for its leadership in digital health research dissemination and commitment to open science principles, emphasizes the significance of this thematic issue as foundational to advancing understanding in this critical domain. By curating a diverse collection of peer-reviewed contributions, the journal aims to stimulate rigorous scientific discourse and influence health policy and management practices globally.

The scope and urgency of this call to action reflect the broader challenges facing healthcare systems worldwide in integrating AI responsibly and effectively. As digital health technologies continue to reshape patient care, reimbursement, and administration, the ability to navigate AI-enabled uncertainty strategically will define the success of future health innovations.

For researchers and practitioners aiming to contribute to this evolving field, the thematic issue represents an opportunity to impact not only academic knowledge but also operational realities. Submissions emphasizing practical, data-informed leadership tactics and governance models that reconcile technological promise with systemic risks will be particularly valued.

Overall, this initiative marks a pivotal moment in the ongoing discourse on AI in healthcare. It underscores the need for collaborative, multidisciplinary efforts to chart pathways through complexity toward more adaptive, equitable, and sustainable health systems in the age of artificial intelligence.

Subject of Research:
Strategic management and governance of AI integration in healthcare systems.

Article Title:
Navigating AI-Enabled Uncertainty – Strategic Implications for Digital Health Management

News Publication Date:
October 22, 2025

Web References:
https://www.jmir.org/announcements/619
https://jmirpublications.com/
https://www.jmir.org/

References:
1. Shaw M. Insurers’ AI denials of postacute care face senate scrutiny. AJMC. Oct 28, 2024.
2. Dyrda L. Amazon vs. Microsoft cloud with Epic: 6 notes. Becker’s Hospital Review. Nov 8, 2024.
3. D’Amico LC. Beyond documentation: building platform moats in healthcare AI. MedCity News. March 13, 2025.
4. Sriharan A, Sekercioglu N, Mitchell C, et al. Leadership for AI transformation in healthcare organization: scoping review. J Med Internet Res. Aug 14, 2024;26:e54556. [doi: 10.2196/54556]

Image Credits:
JMIR Publications, Source: Alexander Sikov

Keywords:
Artificial intelligence, Insurance, Risk management, Health care costs, Health care delivery, Health care policy, Public policy, Digital data

The nuances of these challenges are being addressed by a distinguished research team led by Lifang He and Mooi Choo Chuah, both professors of computer science and engineering at Lehigh University. This expert collaboration extends beyond academia, as it also includes luminaries from clinical settings and industry. Notably, their efforts have culminated in the organization of the “AI for Health Symposium”, which aims to unite experts from a variety of fields to tackle these intricate issues.

Scheduled for March 31 through April 2, 2025, at the San Francisco Airport Marriott Waterfront, the symposium represents a unique opportunity to discuss the intersection of AI and healthcare. It will gather researchers, clinicians, and stakeholders to explore potential solutions that make AI systems not only reliable but also ethical and inclusive. Such gatherings are vital, especially as the landscape of healthcare continues to evolve rapidly.

Moreover, the symposium’s objectives are well-framed. It seeks to foster collaborations across disciplines and promote the development of people-centered, AI-enabled healthcare systems. The importance of collaboration cannot be overstated in a field where technology and human touch must go hand-in-hand for optimal patient outcomes. As Lifang He states, “By bringing together diverse stakeholders, we seek to foster cross-disciplinary collaboration and promote the development of people-centered, AI-enabled healthcare systems.”

In addition to team-led initiatives, the symposium will feature an impressive lineup of keynote speakers. Thought leaders in the fields of medicine and technology, such as Randy Hirschtick from Massachusetts General Hospital/Harvard Medical School, Santosh Kumar from the University of Memphis, and Ruowang Li from Cedars-Sinai Medical Center, are set to share their insights. Their expertise will provide attendees with diverse perspectives on leveraging AI in patient care and also navigating the ethical complexities that accompany such technologies.

The discussions will cover a wide array of topics, ensuring that participants gain a comprehensive understanding of the current challenges and solutions related to AI in healthcare. Areas like medical ethics, data privacy, and machine learning applications will be pivotal themes throughout the symposium. A noteworthy aspect of this dialogue will focus on making AI technologies more inclusive and broadly applicable to various populations, particularly underserved communities who traditionally have limited access to advanced healthcare solutions.

Another anticipated highlight will be a close examination of the data governance protocols needed to ensure that AI systems are both effective and ethical. The collection, use, and sharing of health data must be executed within a framework that protects individual privacy while also allowing for the innovation that AI can drive. This delicate balance is essential to gain public trust, which is a prerequisite for the widespread adoption of AI solutions in healthcare settings.

In this rapidly evolving landscape, machine learning techniques are poised to reshape diagnostic protocols significantly. Innovations in AI-powered diagnostic tools can lead to earlier and more accurate disease detection, which is crucial in treatment planning and improving health outcomes. Rigorous discussions at the symposium will delve into how these advanced technologies can be translated into clinical practice while maintaining an ethical stance.

As AI continues to proliferate in various healthcare domains—from predictive analytics to robotic surgery—its implications extend beyond clinical efficacy. With these advancements come questions regarding accountability and trustworthiness. Symposium participants will engage in vital conversations addressing these ethical quandaries to ensure that AI remains a tool for empowerment rather than exploitation.

AI’s potential to revolutionize healthcare does not come without substantial risks. One of the primary concerns is the possibility of algorithmic bias, which can result in unequal treatment across different demographics. Stakeholders must collaborate to devise frameworks that address these biases head-on to guarantee fairness in AI-driven healthcare solutions.

Finally, the success of AI in healthcare will depend significantly on education and public perception. As technological advancements race ahead, fostering an informed public discourse is crucial. Research initiatives, such as those led by Prof. He, prioritize not just the technology but also the dialogue surrounding it. Educating the next generation of technologists and healthcare providers about responsible AI practices will help ensure sustainable integration of AI solutions in healthcare.

In wrapping up, the “AI for Health Symposium” stands as a pivotal gathering, bringing together diverse expertise and thought leadership. By addressing the critical themes of ethical AI, its governance, and regulatory structures, this event could serve as an innovative model for future interdisciplinary collaborations in healthcare.

Subject of Research: The integration of AI in healthcare systems and its associated challenges.
Article Title: The Transformative Promise of AI in Healthcare: Challenges and Solutions.
News Publication Date: October 12, 2023
Web References: https://engineering.lehigh.edu/faculty/lifang-he, https://engineering.lehigh.edu/faculty/mooi-choo-chuah, https://researchers.mgh.harvard.edu/profile/15451263/Xiang-Li, https://labs.feinberg.northwestern.edu/luolab/
References: None.
Image Credits: Credit: Lehigh University

Keywords

Artificial intelligence, Machine learning, Health and medicine, Medical ethics, Computer science.