Artificial Intelligence In Radiology Research Articles

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

AbstractIn this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical radiology practice and examined health professionals' perspectives regarding AI use in radiology. This review followed the Joanna Briggs Institute (JBI) methodological guidelines. We searched multiple databases and the gray literature from March 15, 2016 to December 31, 2023. Of 49 articles reviewed, 13 assessed the performance of AI in radiology clinical practice, and 36 examined the attitudes of health professionals toward the use of AI in radiology. In four separate studies, AI significantly improved the diagnostic sensitivity or detection rate. Furthermore, six articles emphasized a significant reduction in case reading times with AI use. Although three studies suggested an increase in specificity with the assistance of AI, these findings did not reach statistical significance. Health professionals expressed the belief that AI would have a significant impact on radiology but would not replace radiologists in the near future. Limited knowledge of AI was observed among health professionals, who supported increased education and explicit regulations and guidelines related to AI. Overall, AI can enhance diagnostic efficiency and accuracy in clinical radiology practice. However, knowledge gaps and the concerns of health professionals should be addressed by prioritizing education and reinforcing ethical and legal regulations to facilitate the advancement of AI use in radiology. This scoping review provides evidence toward a comprehensive understanding of AI's potential in clinical radiology practice, promoting its use and stimulating further discussion on related challenges and implications.

Beyond the Clinic Walls: Examining Radiology Technicians' Experiences in Home-Based Radiography.

In recent years, the landscape of diagnostic imaging has undergone a significant transformation with the emergence of home radiology, challenging the traditional paradigm. This shift, bringing diagnostic imaging directly to patients, has gained momentum and has been further accelerated by the global COVID-19 pandemic, highlighting the increasing importance and convenience of decentralized healthcare services. This study aims to offer a nuanced understanding of the attitudes and experiences influencing the integration of in-home radiography into contemporary healthcare practices. The research methodology involves a survey administered through Computer-Aided Web Interviewing (CAWI) tools, enabling real-time engagement with a diverse cohort of medical radiology technicians in the health domain. A second CAWI tool is submitted to experts to assess their feedback on the methodology. The survey explores key themes, including perceived advantages and challenges associated with domiciliary imaging, its impact on patient care, and the technological intricacies specific to conducting radiologic procedures outside the conventional clinical environment. Findings from a sample of 26 medical radiology technicians (drawn from a larger pool of 186 respondents) highlight a spectrum of opinions and constructive feedback. Enthusiasm is evident for the potential of domiciliary imaging to enhance patient convenience and provide a more patient-centric approach to healthcare. Simultaneously, this study suggests areas of intervention to improve the diffusion of home-based radiology. The methodology based on CAWI tools proves instrumental in the efficiency and depth of data collection, as evaluated by 16 experts from diverse professional backgrounds. The dynamic and responsive nature of this approach allows for a more allocated exploration of technicians' opinions, contributing to a comprehensive understanding of the evolving landscape of medical imaging services. Emphasis is placed on the need for national and international initiatives in the field, supported by scientific societies, to further explore the evolving landscape of teleradiology and the integration of artificial intelligence in radiology. This study encourages expansion involving other key figures in this practice, including, naturally, medical radiologists, general practitioners, medical physicists, and other stakeholders.

Adoption, orchestration, and deployment of artificial intelligence within the National Health Service—facilitators and barriers: an expert roundtable discussion

Abstract Clinical care, workflow, and patient outcomes within National Health Service (NHS) radiology services will potentially benefit significantly in coming years with the integration of artificial intelligence (AI) into various specialty areas. This article summarizes an expert roundtable discussion, convened by the British Institute of Radiology, comprised experts from clinical, NHS management and industry backgrounds. Facilitators and barriers to both implementation and integration of AI platforms into daily practice are discussed alongside examples including AI in emergency care and breast screening. Issues addressed include the need to generate more real-world evidence that helps developers and service providers to determine the optimal way for patients to reap the benefits of AI with faster reporting turnaround time, faster diagnoses, and treatment times, for example. To ensure the effective and timely wide-scale implementation, adoption requires a national policy drive and national level coordination but specific, local needs of NHS Trusts and any particular service within Trusts will also need to be met. By embedding AI-capabilities into scanners, ultrasound-devices, X-ray equipment, and Picture archiving and communication system (PACS), local services will benefit, while AI applications can streamline workflows across and between departments and potentially Trusts. AI predictive analytics and systems engineering can be used to better manage patient volume and movement. In the short term, education, training, and confidence in adapting and adopting AI in radiology require dedicated local, national, and governmental effort but the future benefits promise to justify the means.

AI and machine learning in medical imaging: key points from development to translation.

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI

Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: (1) AI product development and (2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economic, and health care settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods conducted by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed.

Environmental Sustainability and AI in Radiology: A Double-Edged Sword.

According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.

A holistic approach to implementing artificial intelligence in radiology

ObjectiveDespite the widespread recognition of the importance of artificial intelligence (AI) in healthcare, its implementation is often limited. This article aims to address this implementation gap by presenting insights from an in-depth case study of an organisation that approached AI implementation with a holistic approach.Materials and methodsWe conducted a longitudinal, qualitative case study of the implementation of AI in radiology at a large academic medical centre in the Netherlands for three years. Collected data consists of 43 days of work observations, 30 meeting observations, 18 interviews and 41 relevant documents. Abductive reasoning was used for systematic data analysis, which revealed three change initiative themes responding to specific AI implementation challenges.ResultsThis study identifies challenges of implementing AI in radiology at different levels and proposes a holistic approach to tackle those challenges. At the technology level, there is the issue of multiple narrow AI applications with no standard use interface; at the workflow level, AI results allow limited interaction with radiologists; at the people and organisational level, there are divergent expectations and limited experience with AI. The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.ConclusionThis study highlights the importance of a holistic approach to AI implementation that addresses challenges spanning technology, workflow, and organisational levels. Aligning change initiatives between these different levels has proven to be important to facilitate wide-scale implementation of AI in clinical practice.Critical relevance statementAdoption of artificial intelligence is crucial for future-ready radiological care. This case study highlights the importance of a holistic approach that addresses technological, workflow, and organisational aspects, offering practical insights and solutions to facilitate successful AI adoption in clinical practice.Key points1. Practical and actionable insights into successful AI implementation in radiology are lacking.2. Aligning technology, workflow, organisational aspects is crucial for a successful AI implementation3. Holistic approach aids organisations to create sustainable value through AI implementation.Graphical

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. Key Points•The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.•Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.•AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist

ObjectivesTo present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists.MethodsThe AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents’ knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants’ expectations and progress and were analyzed using a Wilcoxon rank-sum test.ResultsThere was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants’ confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful.ConclusionDesigning an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants’ perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization.Critical relevance statementThe framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care.Key points• AI education is necessary to prepare a new generation of AI-conscious radiologists.• The AI curriculum increased participants’ perception of AI knowledge and skills in radiology.• This five-step framework can assist integrating AI education into radiology residency programs.Graphical

Artificial intelligence in radiology and radiotherapy

Artificial intelligence in radiology and radiotherapy

Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology.

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

The evolving landscape: Role of artificial intelligence in cancer detection

<abstract> <p>Artificial intelligence (AI) has played a major role in recent developments in healthcare, particularly in cancer diagnosis. This review investigated the dynamic role of AI in the detection of cancer and provides insights into the fundamental contributions of AI in the revolutionization of cancer detection methodologies, focusing on the role of AI in radiology and medical imaging and highlighting AI's advancements in enhancing accuracy and efficiency in identifying cancerous lesions. Furthermore, it explained the indispensable role of pathology and histopathology in cancer diagnosis, emphasizing AI's potential to augment traditional methods and improve diagnostic precision. Genomics and personalized medicine were explored as integral components of cancer detection, illustrating how AI facilitates tailored treatment strategies by analyzing vast genomic datasets. Additionally, the discussion encompassed clinical decision support systems, explaining their utility in aiding healthcare professionals with evidence-based insights for more informed decision-making in cancer detection and management. Finally, the review addressed the challenges and future directions in the integration of AI into cancer detection practices, highlighting the need for continued research and development to overcome existing limitations and realize the full potential of AI-driven solutions in combating cancer.</p> </abstract>

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Revolutionizing Radiology: Exploring Applications and Advancements in AI for Imaging Diagnostics

Artificial intelligence (AI) has revolutionized the field of medical imaging diagnostics, offering unprecedented opportunities for accurate and efficient disease detection and diagnosis. The field of radiology is undergoing a profound transformation due to the rapid advancements in artificial intelligence (AI). AI algorithms, particularly deep learning, have demonstrated remarkable capabilities in analyzing medical images, leading to significant improvements in diagnostic accuracy, efficiency, and patient care. This paper provides a comprehensive overview of the current landscape of AI in radiology, exploring its diverse applications and the groundbreaking advancements that are revolutionizing the field.

The Impact of Artificial Intelligence on the Future of Radiographer Career in Saudi Arabia

The emergence of artificial intelligence (AI) technologies in the healthcare imaging domain has heightened discourse regarding the responsibilities and role of radiologists, thereby bringing this issue to the fore. On the other hand, the extent to which this kind of equipment will alter the radiographer's job has not been much discussed. To examine the potential effects of AI on the radiography job, this study assesses current processes and cross-maps potential areas for automation, such as procedure organizing, picture capture, and processing images. We also draw attention to the advantages that AI offers, such as improved patient care, greater technology skills, elevated cross-modality learning and teamwork, and an extension of radiographer duties into AI-supported picture auditing and reporting roles. For the purpose of incorporating AI systems into medical imaging, radiologists are crucial because they serve as a liaison between people and technological advances. Studies have looked at the knowledge, beliefs, and attitudes of Saudi Arabian radiology professionals toward AI to investigate how radiologists react toward AI in radiology and to find out their opinions about AI in the future of radiology.

Harnessing artificial intelligence in radiology to augment population health.

This review article serves to highlight radiological services as a major cost driver for the healthcare sector, and the potential improvements in productivity and cost savings that can be generated by incorporating artificial intelligence (AI) into the radiology workflow, referencing Singapore healthcare as an example. More specifically, we will discuss the opportunities for AI in lowering healthcare costs and supporting transformational shifts in our care model in the following domains: predictive analytics for optimising throughput and appropriate referrals, computer vision for image enhancement (to increase scanner efficiency and decrease radiation exposure) and pattern recognition (to aid human interpretation and worklist prioritisation), natural language processing and large language models for optimising reports and text data-mining. In the context of preventive health, we will discuss how AI can support population level screening for major disease burdens through opportunistic screening and democratise expertise to increase access to radiological services in primary and community care.

Self-supervised multi-modal training from uncurated images and reports enables monitoring AI in radiology

The escalating demand for artificial intelligence (AI) systems that can monitor and supervise human errors and abnormalities in healthcare presents unique challenges. Recent advances in vision-language models reveal the challenges of monitoring AI by understanding both visual and textual concepts and their semantic correspondences. However, there has been limited success in the application of vision-language models in the medical domain. Current vision-language models and learning strategies for photographic images and captions call for a web-scale data corpus of image and text pairs which is not often feasible in the medical domain. To address this, we present a model named medical cross-attention vision-language model (Medical X-VL), which leverages key components to be tailored for the medical domain. The model is based on the following components: self-supervised unimodal models in medical domain and a fusion encoder to bridge them, momentum distillation, sentencewise contrastive learning for medical reports, and sentence similarity-adjusted hard negative mining. We experimentally demonstrated that our model enables various zero-shot tasks for monitoring AI, ranging from the zero-shot classification to zero-shot error correction. Our model outperformed current state-of-the-art models in two medical image datasets, suggesting a novel clinical application of our monitoring AI model to alleviate human errors. Our method demonstrates a more specialized capacity for fine-grained understanding, which presents a distinct advantage particularly applicable to the medical domain.

Advances in imaging: Exploring the potential of artificial intelligence in radiology

Radiology has seen significant advances in its quest to move from descriptive to diagnostic, and more recently to predictive. The long-standing reliance on traditional 2D X-rays has evolved into the application of high-powered magnets and CT scanners, which provide improved tissue contrast and 3D capabilities. These advances have led to more accurate identification of diagnostic imaging markers and increased the ability to identify biomarkers for diagnostic and predictive imaging. While these advances still fall within the realm of human interpretation, there is continued momentum to accelerate efforts that are reshaping the clinical workplace. Whether it is the need to harness the vast and largely untapped potential of artificial intelligence, the need to increase efficiency and reduce reading times, or the call to creatively improve patient care by integrating imaging and biomarker-related technologies, the community is pushing the discipline to evolve further. To help bring these necessary tools to clinical applications, collaborative efforts between clinicians, data scientists, and engineers will need to come together. The scientific community must work together to enable the clinical translation of technologies that combine diverse physical and biological imaging findings.

Clinical applications of artificial intelligence in Radiology: prostate magnetic resonance imaging

Clinical applications of artificial intelligence in Radiology: prostate magnetic resonance imaging