Artificial Intelligence in Radiology-Ethical Considerations

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review

BACKGROUND

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention.

METHODS

We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs.

RESULTS

We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%.

CONCLUSIONS

AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review

Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.

Robotics and AI into healthcare from the perspective of European regulation: who is responsible for medical malpractice?

The integration of robotics and artificial intelligence into medical practice is radically revolutionising patient care. This fusion of advanced technologies with healthcare offers a number of significant benefits, including more precise diagnoses, personalised treatments and improved health data management. However, it is critical to address very carefully the medico-legal challenges associated with this progress. The responsibilities between the different players concerned in medical liability cases are not yet clearly defined, especially when artificial intelligence is involved in the decision-making process. Complexity increases when technology intervenes between a person's action and the result, making it difficult for the patient to prove harm or negligence. In addition, there is the risk of an unfair distribution of blame between physicians and healthcare institutions. The analysis of European legislation highlights the critical issues related to the attribution of legal personality to autonomous robots and the recognition of strict liability for medical doctors and healthcare institutions. Although European legislation has helped to standardise the rules on this issue, some questions remain unresolved. We argue that specific laws are needed to address the issue of medical liability in cases where robotics and artificial intelligence are used in healthcare.

Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review

Cone-beam computed tomography (CBCT) has emerged as a promising tool for the analysis of the upper airway, leveraging on its ability to provide three-dimensional information, minimal radiation exposure, affordability, and widespread accessibility. The integration of artificial intelligence (AI) in CBCT for airway analysis has shown improvements in the accuracy and efficiency of diagnosing and managing airway-related conditions. This review aims to explore the current applications of AI in CBCT for airway analysis, highlighting its components and processes, applications, benefits, challenges, and potential future directions. A comprehensive literature review was conducted, focusing on studies published in the last decade that discuss AI applications in CBCT airway analysis. Many studies reported the significant improvement in segmentation and measurement of airway volumes from CBCT using AI, thereby facilitating accurate diagnosis of airway-related conditions. In addition, these AI models demonstrated high accuracy and consistency in their application for airway analysis through automated segmentation tasks, volume measurement, and 3D reconstruction, which enhanced the diagnostic accuracy and allowed predictive treatment outcomes. Despite these advancements, challenges remain in the integration of AI into clinical workflows. Furthermore, variability in AI performance across different populations and imaging settings necessitates further validation studies. Continued research and development are essential to overcome current challenges and fully realize the potential of AI in airway analysis.

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study

BACKGROUND

Diagnosing myocarditis relies on multimodal data, including cardiovascular magnetic resonance (CMR), clinical symptoms, and blood values. The correct interpretation and integration of CMR findings require radiological expertise and knowledge. We aimed to investigate the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model, for report-based medical decision-making in the context of cardiac MRI for suspected myocarditis.

METHODS

This retrospective study includes CMR reports from 396 patients with suspected myocarditis and eight centers, respectively. CMR reports and patient data including blood values, age, and further clinical information were provided to GPT-4 and radiologists with 1 (resident 1), 2 (resident 2), and 4 years (resident 3) of experience in CMR and knowledge of the 2018 Lake Louise Criteria. The final impression of the report regarding the radiological assessment of whether myocarditis is present or not was not provided. The performance of Generative pre-trained transformer 4 (GPT-4) and the human readers were compared to a consensus reading (two board-certified radiologists with 8 and 10 years of experience in CMR). Sensitivity, specificity, and accuracy were calculated.

RESULTS

GPT-4 yielded an accuracy of 83%, sensitivity of 90%, and specificity of 78%, which was comparable to the physician with 1 year of experience (R1: 86%, 90%, 84%, p = 0.14) and lower than that of more experienced physicians (R2: 89%, 86%, 91%, p = 0.007 and R3: 91%, 85%, 96%, p < 0.001). GPT-4 and human readers showed a higher diagnostic performance when results from T1- and T2-mapping sequences were part of the reports, for residents 1 and 3 with statistical significance (p = 0.004 and p = 0.02, respectively).

CONCLUSION

GPT-4 yielded good accuracy for diagnosing myocarditis based on CMR reports in a large dataset from multiple centers and therefore holds the potential to serve as a diagnostic decision-supporting tool in this capacity, particularly for less experienced physicians. Further studies are required to explore the full potential and elucidate educational aspects of the integration of large language models in medical decision-making.

Errors in Radiology: A Standard Review

Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy.

Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.

The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care

The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.

Comparison of an AI-Generated Case Report With a Human-Written Case Report: Practical Considerations for AI-Assisted Medical Writing

INTRODUCTION

 The utility of ChatGPT has recently caused consternation in the medical world. While it has been utilized to write manuscripts, only a few studies have evaluated the quality of manuscripts generated by AI (artificial intelligence).

OBJECTIVE

 We evaluate the ability of ChatGPT to write a case report when provided with a framework. We also provide practical considerations for manuscript writing using AI.

METHODS

We compared a manuscript written by a blinded human author (10 years of medical experience) with a manuscript written by ChatGPT on a rare presentation of a common disease. We used multiple iterations of the manuscript generation request to derive the best ChatGPT output. Participants, outcomes, and measures: 22 human reviewers compared the manuscripts using parameters that characterize human writing and relevant standard manuscript assessment criteria, viz., scholarly impact quotient (SIQ). We also compared the manuscripts using the "average perplexity score" (APS), "burstiness score" (BS), and "highest perplexity of a sentence" (GPTZero parameters to detect AI-generated content).

RESULTS

The human manuscript had a significantly higher quality of presentation and nuanced writing (p<0.05). Both manuscripts had a logical flow. 12/22 reviewers were able to identify the AI-generated manuscript (p<0.05), but 4/22 reviewers wrongly identified the human-written manuscript as AI-generated. GPTZero software erroneously identified four sentences of the human-written manuscript to be AI-generated.

CONCLUSION

 Though AI showed an ability to highlight the novelty of the case report and project a logical flow comparable to the human manuscript, it could not outperform the human writer on all parameters. The human manuscript showed a better quality of presentation and more nuanced writing. The practical considerations we provide for AI-assisted medical writing will help to better utilize AI in manuscript writing.

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

Current applications and future potential of ChatGPT in radiology: A systematic review

This study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI-based chat model, in the field of radiology. The primary focus is on its role in enhancing decision-making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision-making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board-style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision-making and optimizing workflow. While ChatGPT's promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology.

The unintended consequences of artificial intelligence in paediatric radiology

Over the past decade, there has been a dramatic rise in the interest relating to the application of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession. In this article, we provide an overview of the latest opinions regarding AI ethics, bias, limitations, challenges and considerations that we should all contemplate in this exciting and expanding field, with a special attention to how this applies to the unique aspects of a paediatric population. By embracing AI technology and fostering a multidisciplinary approach, it is hoped that we can harness the power AI brings whilst minimising harm and ensuring a beneficial impact on radiology practice.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis

PURPOSE

This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data.

METHODS

Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks' funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA.

RESULTS

From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87-0.96). The DORs were 103 (27-251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis.

CONCLUSION

With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity.

A Short Review on the Impact of Artificial Intelligence in Diagnosis Diseases: Role of Radiomics In Neuro-Oncology

Artificial Intelligence (AI) is rapidly transforming various aspects of healthcare, including the field of diagnostics and treatment of diseases. This review article aimed to provide an in-depth analysis of the impact of AI, especially, radiomics in the diagnosis of neuro-oncology diseases. Indeed, it is a multidimensional task that requires the integration of clinical assessment, neuroimaging techniques, and emerging technologies like AI and radiomics. The advancements in these fields have the potential to revolutionize the accuracy, efficiency, and personalized approach to diagnosing neuro-oncology diseases, leading to improved patient outcomes and enhanced overall neurologic care. However, AI has some limitations, and ethical challenges should be addressed via future research.

Ethical implications of AI and robotics in healthcare: A review

Integrating Artificial Intelligence (AI) and robotics in healthcare heralds a new era of medical innovation, promising enhanced diagnostics, streamlined processes, and improved patient care. However, this technological revolution is accompanied by intricate ethical implications that demand meticulous consideration. This article navigates the complex ethical terrain surrounding AI and robotics in healthcare, delving into specific dimensions and providing strategies and best practices for ethical navigation. Privacy and data security are paramount concerns, necessitating robust encryption and anonymization techniques to safeguard patient data. Responsible data handling practices, including decentralized data sharing, are critical to preserve patient privacy. Algorithmic bias poses a significant challenge, demanding diverse datasets and ongoing monitoring to ensure fairness. Transparency and explainability in AI decision-making processes enhance trust and accountability. Clear responsibility frameworks are essential to address the accountability of manufacturers, healthcare institutions, and professionals. Ethical guidelines, regularly updated and accessible to all stakeholders, guide decision-making in this dynamic landscape. Moreover, the societal implications of AI and robotics extend to accessibility, equity, and societal trust. Strategies to bridge the digital divide and ensure equitable access must be prioritized. Global collaboration is pivotal in developing adaptable regulations and addressing legal challenges like liability and intellectual property. Ethics must remain at the forefront in the ever-evolving realm of healthcare technology. By embracing these strategies and best practices, healthcare systems and professionals can harness the potential of AI and robotics, ensuring responsible and ethical integration that benefits patients while upholding the highest ethical standards.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam

The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.

A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare

Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance

The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.

Revolution of echocardiographic reporting: the new era of artificial intelligence and natural language processing

Artificial intelligence (AI) has been making a significant impact on cardiovascular imaging, transforming everything from data capture to report generation. In the field of echocardiography, AI offers the potential to enhance accuracy, speed up reporting, and reduce the workload of physicians. This is an advantage because, compared to computed tomography and magnetic resonance imaging, echocardiograms tend to exhibit higher observer variability in interpretation. This review takes a comprehensive viewpoint at AI-based reporting systems and their application in echocardiography, emphasizing the need for automated diagnoses. The integration of natural language processing (NLP) technologies, including ChatGPT, could provide revolutionary advancements. One of the exciting prospects of AI integration is its potential to accelerate reporting, thereby improving patient outcomes and access to treatment, while also mitigating physician burnout. However, AI introduces new challenges like ensuring data quality, managing potential over-reliance on AI, addressing legal and ethical concerns, and balancing significant costs against benefits. As we navigate these complexities, it's important for cardiologists to stay updated with AI advancements and learn to utilize them effectively. AI has the potential to be integrated into daily clinical practice, becoming a valuable tool for healthcare professionals dealing with heart diseases, provided it's approached with careful consideration.

Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

Explainable AI in radiology: a white paper of the Italian Society of Medical and Interventional Radiology

The term Explainable Artificial Intelligence (xAI) groups together the scientific body of knowledge developed while searching for methods to explain the inner logic behind the AI algorithm and the model inference based on knowledge-based interpretability. The xAI is now generally recognized as a core area of AI. A variety of xAI methods currently are available to researchers; nonetheless, the comprehensive classification of the xAI methods is still lacking. In addition, there is no consensus among the researchers with regards to what an explanation exactly is and which are salient properties that must be considered to make it understandable for every end-user. The SIRM introduces an xAI-white paper, which is intended to aid Radiologists, medical practitioners, and scientists in the understanding an emerging field of xAI, the black-box problem behind the success of the AI, the xAI methods to unveil the black-box into a glass-box, the role, and responsibilities of the Radiologists for appropriate use of the AI-technology. Due to the rapidly changing and evolution of AI, a definitive conclusion or solution is far away from being defined. However, one of our greatest responsibilities is to keep up with the change in a critical manner. In fact, ignoring and discrediting the advent of AI a priori will not curb its use but could result in its application without awareness. Therefore, learning and increasing our knowledge about this very important technological change will allow us to put AI at our service and at the service of the patients in a conscious way, pushing this paradigm shift as far as it will benefit us.

Ethical and legal challenges in nanomedical innovations: a scoping review

Background: Rapid advancements in research and development related to nanomedical technology raise various ethical and legal challenges in areas relevant to disease detection, diagnosis, and treatment. This study aims to outline the existing literature, covering issues associated with emerging nanomedicine and related clinical research, and identify implications for the responsible advancement and integration of nanomedicine and nanomedical technology throughout medical networks in the future. Methods: A scoping review, designed to cover scientific, ethical, and legal literature associated with nanomedical technology, was conducted, generating and analyzing 27 peer-reviewed articles published between 2007-2020. Results: Results indicate that articles referencing ethical and legal issues related to nanomedical technology were concerned with six key areas: 1) harm exposure and potential risks to health, 2) consent to nano-research, 3) privacy, 4) access to nanomedical technology and potential nanomedical therapies, 5) classification of nanomedical products in relation to the research and development of nanomedical technology, and 6) the precautionary principle as it relates to the research and development of nanomedical technology. Conclusion: This review of the literature suggests that few practical solutions are comprehensive enough to allay the ethical and legal concerns surrounding research and development in fields related to nanomedical technology, especially as it continues to evolve and contribute to future innovations in medicine. It is also clearly apparent that a more coordinated approach is required to ensure global standards of practice governing the study and development of nanomedical technology, especially as discussions surrounding the regulation of nanomedical research throughout the literature are mainly confined to systems of governance in the United States.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Ebenen der Explizierbarkeit für medizinische künstliche Intelligenz: Was brauchen wir normativ und was können wir technisch erreichen?

Definition of the problem

The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI?

Arguments

We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example.

Conclusion

We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age

A new task force dedicated to artificial intelligence (AI) with respect to paediatric radiology was created in 2021 at the International Paediatric Radiology (IPR) meeting in Rome, Italy (a joint society meeting by the European Society of Pediatric Radiology [ESPR] and the Society for Pediatric Radiology [SPR]). The concept of a separate task force dedicated to AI was borne from an ESPR-led international survey of health care professionals' opinions, expectations and concerns regarding AI integration within children's imaging departments. In this survey, the majority (> 80%) of ESPR respondents supported the creation of a task force and helped define our key objectives. These include providing educational content about AI relevant for paediatric radiologists, brainstorming ideas for future projects and collaborating on AI-related studies with respect to collating data sets, de-identifying images and engaging in multi-case, multi-reader studies. This manuscript outlines the starting point of the ESPR AI task force and where we wish to go.

AI: Can It Make a Difference to the Predictive Value of Ultrasound Breast Biopsy?

(1) Background: This study aims to compare the ground truth (pathology results) against the BI-RADS classification of images acquired while performing breast ultrasound diagnostic examinations that led to a biopsy and against the result of processing the same images through the AI algorithm KOIOS DS ^TM (KOIOS). (2) Methods: All results of biopsies performed with ultrasound guidance during 2019 were recovered from the pathology department. Readers selected the image which better represented the BI-RADS classification, confirmed correlation to the biopsied image, and submitted it to the KOIOS AI software. The results of the BI-RADS classification of the diagnostic study performed at our institution were set against the KOIOS classification and both were compared to the pathology reports. (3) Results: 403 cases were included in this study. Pathology rendered 197 malignant and 206 benign reports. Four biopsies on BI-RADS 0 and two images are included. Of fifty BI-RADS 3 cases biopsied, only seven rendered cancers. All but one had a positive or suspicious cytology; all were classified as suspicious by KOIOS. Using KOIOS, 17 B3 biopsies could have been avoided. Of 347 BI-RADS 4, 5, and 6 cases, 190 were malignant (54.7%). Because only KOIOS suspicious and probably malignant categories should be biopsied, 312 biopsies would have resulted in 187 malignant lesions (60%), but 10 cancers would have been missed. (4) Conclusions: KOIOS had a higher ratio of positive biopsies in this selected case study vis-à-vis the BI-RADS 4, 5 and 6 categories. A large number of biopsies in the BI-RADS 3 category could have been avoided.

Artificial Intelligence (AI) for Fracture Diagnosis: An Overview of Current Products and Considerations for Clinical Adoption, From the AJR Special Series on AI Applications

Fractures are common injuries that can be difficult to diagnose, with missed fractures accounting for most misdiagnoses in the emergency department. Artificial intelligence (AI) and, specifically, deep learning have shown a strong ability to accurately detect fractures and augment the performance of radiologists in proof-of-concept research settings. Although the number of real-world AI products available for clinical use continues to increase, guidance for practicing radiologists in the adoption of this new technology is limited. This review describes how AI and deep learning algorithms can help radiologists to better diagnose fractures. The article also provides an overview of commercially available U.S. FDA-cleared AI tools for fracture detection as well as considerations for the clinical adoption of these tools by radiology practices.

First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology

Artificial Intelligence and machine learning are novel technologies that will change the way veterinary medicine is practiced. Exactly how this change will occur is yet to be determined, and, as is the nature with disruptive technologies, will be difficult to predict. Ushering in this new tool in a conscientious way will require knowledge of the terminology and types of AI as well as forward thinking regarding the ethical and legal implications within the profession. Developers as well as end users will need to consider the ethical and legal components alongside functional creation of algorithms in order to foster acceptance and adoption, and most importantly to prevent patient harm. There are key differences in deployment of these technologies in veterinary medicine relative to human healthcare, namely our ability to perform euthanasia, and the lack of regulatory validation to bring these technologies to market. These differences along with others create a much different landscape than AI use in human medicine, and necessitate proactive planning in order to prevent catastrophic outcomes, encourage development and adoption, and protect the profession from unnecessary liability. The authors offer that deploying these technologies prior to considering the larger ethical and legal implications and without stringent validation is putting the AI cart before the horse, and risks putting patients and the profession in harm's way.

Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare-A Scoping Review of Reviews

BACKGROUND

With the availability of extensive health data, artificial intelligence has an inordinate capability to expedite medical explorations and revamp healthcare.Artificial intelligence is set to reform the practice of medicine soon. Despite the mammoth advantages of artificial intelligence in the medical field, there exists inconsistency in the ethical and legal framework for the application of AI in healthcare. Although research has been conducted by various medical disciplines investigating the ethical implications of artificial intelligence in the healthcare setting, the literature lacks a holistic approach.

OBJECTIVE

The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework.

METHODOLOGY

Electronic databases Pub Med and Google Scholar were extensively searched based on the search strategy pertaining to the purpose of this review. Further screening of the included articles was done on the grounds of the inclusion and exclusion criteria.

RESULTS

The search yielded a total of 1238 articles, out of which 16 articles were identified to be eligible for this review. The selection was strictly based on the inclusion and exclusion criteria mentioned in the manuscript.

CONCLUSION

Artificial intelligence (AI) is an exceedingly puissant technology, with the prospect of advancing medical practice in the years to come. Nevertheless, AI brings with it a colossally abundant number of ethical and legal problems associated with its application in healthcare. There are manifold stakeholders in the legal and ethical issues revolving around AI and medicine. Thus, a multifaceted approach involving policymakers, developers, healthcare providers and patients is crucial to arrive at a feasible solution for mitigating the legal and ethical problems pertaining to AI in healthcare.

Turing test-inspired method for analysis of biases prevalent in artificial intelligence-based medical imaging

Due to the growing need to provide better global healthcare, computer-based and robotic healthcare equipment that depend on artificial intelligence has seen an increase in development. In order to evaluate artificial intelligence (AI) in computer technology, the Turing test was created. For evaluating the future generation of medical diagnostics and medical robots, it remains an essential qualitative instrument. We propose a novel methodology to assess AI-based healthcare technology that provided verifiable diagnostic accuracy and statistical robustness. In order to run our test, we used a state-of-the-art AI model and compared it to radiologists for checking how generalized the model is and if any biases are prevalent. We achieved results that can evaluate the performance of our chosen model for this study in a clinical setting and we also applied a quantifiable method for evaluating our modified Turing test results using a meta-analytical evaluation framework. His test provides a translational standard for upcoming AI modalities. Our modified Turing test is a notably strong standard to measure the actual performance of the AI model on a variety of edge cases and normal cases and also helps in detecting if the algorithm is biased towards any one type of case. This method extends the flexibility to detect any prevalent biases and also classify the type of bias.

Artificial intelligence in pancreatic cancer

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence

The integration of human and machine intelligence promises to profoundly change the practice of medicine. The rapidly increasing adoption of artificial intelligence (AI) solutions highlights its potential to streamline physician work and optimize clinical decision-making, also in the field of pediatric radiology. Large imaging databases are necessary for training, validating and testing these algorithms. To better promote data accessibility in multi-institutional AI-enabled radiologic research, these databases centralize the large volumes of data required to effect accurate models and outcome predictions. However, such undertakings must consider the sensitivity of patient information and therefore utilize requisite data governance measures to safeguard data privacy and security, to recognize and mitigate the effects of bias and to promote ethical use. In this article we define data stewardship and data governance, review their key considerations and applicability to radiologic research in the pediatric context, and consider the associated best practices along with the ramifications of poorly executed data governance. We summarize several adaptable data governance frameworks and describe strategies for their implementation in the form of distributed and centralized approaches to data management.

Ethics of AI in Radiology: A Review of Ethical and Societal Implications

Artificial intelligence (AI) is being applied in medicine to improve healthcare and advance health equity. The application of AI-based technologies in radiology is expected to improve diagnostic performance by increasing accuracy and simplifying personalized decision-making. While this technology has the potential to improve health services, many ethical and societal implications need to be carefully considered to avoid harmful consequences for individuals and groups, especially for the most vulnerable populations. Therefore, several questions are raised, including (1) what types of ethical issues are raised by the use of AI in medicine and biomedical research, and (2) how are these issues being tackled in radiology, especially in the case of breast cancer? To answer these questions, a systematic review of the academic literature was conducted. Searches were performed in five electronic databases to identify peer-reviewed articles published since 2017 on the topic of the ethics of AI in radiology. The review results show that the discourse has mainly addressed expectations and challenges associated with medical AI, and in particular bias and black box issues, and that various guiding principles have been suggested to ensure ethical AI. We found that several ethical and societal implications of AI use remain underexplored, and more attention needs to be paid to addressing potential discriminatory effects and injustices. We conclude with a critical reflection on these issues and the identified gaps in the discourse from a philosophical and STS perspective, underlining the need to integrate a social science perspective in AI developments in radiology in the future.

Precision Imaging Guidance in the Era of Precision Oncology: An Update of Imaging Tools for Interventional Procedures

Interventional oncology (IO) procedures have become extremely popular in interventional radiology (IR) and play an essential role in the diagnosis, treatment, and supportive care of oncologic patients through new and safe procedures. IR procedures can be divided into two main groups: vascular and non-vascular. Vascular approaches are mainly based on embolization and concomitant injection of chemotherapeutics directly into the tumor-feeding vessels. Percutaneous approaches are a type of non-vascular procedures and include percutaneous image-guided biopsies and different ablation techniques with radiofrequency, microwaves, cryoablation, and focused ultrasound. The use of these techniques requires precise imaging pretreatment planning and guidance that can be provided through different imaging techniques: ultrasound, computed tomography, cone-beam computed tomography, and magnetic resonance. These imaging modalities can be used alone or in combination, thanks to fusion imaging, to further improve the confidence of the operators and the efficacy and safety of the procedures. This article aims is to provide an overview of the available IO procedures based on clinical imaging guidance to develop a targeted and optimal approach to cancer patients.

Teachers’ Perceptions of Teaching Sustainable Artificial Intelligence: A Design Frame Perspective

Teaching artificial intelligence (AI) is an emerging challenge in global school education. There are considerable barriers to overcome, including the existing practices of technology education and teachers’ knowledge of AI. Research evidence shows that studying teachers’ experiences can be beneficial in informing how appropriate design in teaching sustainable AI should evolve. Design frames characterize teachers’ design reasoning and can substantially influence their AI lesson design considerations. This study examined 18 experienced teachers’ perceptions of teaching AI and identified effective designs to support AI instruction. Data collection methods involved semi-structured interviews, action study, classroom observation, and post-lesson discussions with the purpose of analyzing the teachers’ perceptions of teaching AI. Grounded theory was employed to detail how teachers understand the pedagogical challenges of teaching AI and the emerging pedagogical solutions from their perspectives. Results reveal that effective AI instructional design should encompass five important components: (1) obstacles to and facilitators of participation in teaching AI, (2) interactive design thinking processes, (3) teachers’ knowledge of teaching AI, (4) orienteering AI knowledge for social good, and (5) the holistic understanding of teaching AI. The implications for future teacher AI professional development activities are proposed.

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

The concept of the invisible radiologist in the era of artificial intelligence

The radiologists were traditionally working in the background. What upgraded them as physicians during the second half of the past century was their clinical training and function precipitated by the evolution of Interventional Radiology and Medical Imaging, especially with ultrasonography. These allowed them to participate in patient's diagnosis and treatment by direct contact as well asvia multidisciplinary medical consultations. The wide application of teleradiology and PACS pushed radiologists back again which is no longer acceptable, especially in view of the amazing applications of artificial intelligence (AI) in Radiology. It is our belief that clinical radiologists have to be able to control the penetration of AI in Radiology, securing their work for the benefit of both clinicians and patients.

Current applications and development of artificial intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception

Background: The emerging field of artificial intelligence (AI) will probably affect the practice for the next generation of doctors. However, the students' views on AI have not been largely investigated. Methods: An anonymous electronic survey on AI was designed for medical and dental students to explore: (1) sources of information about AI, (2) AI applications and concerns, (3) AI status as a topic in medicine, and (4) students' feelings and attitudes. The questionnaire was advertised on social media platforms in 2020. Security measures were employed to prevent fraudulent responses. Mann-Whitney U-test was employed for all comparisons. A sensitivity analysis was also performed by binarizing responses to express disagreement and agreement using the Chi-squared test. Results: Three thousand one hundred thirty-three respondents from 63 countries from all continents were included. Most respondents reported having at least a moderate understanding of the technologies underpinning AI and of their current application, with higher agreement associated with being male (p < 0.0001), tech-savvy (p < 0.0001), pre-clinical student (p < 0.006), and from a developed country (p < 0.04). Students perceive AI as a partner rather than a competitor (72.2%) with a higher agreement for medical students (p = 0.002). The belief that AI will revolutionize medicine and dentistry (83.9%) with greater agreement for students from a developed country (p = 0.0004) was noted. Most students agree that the AI developments will make medicine and dentistry more exciting (69.9%), that AI shall be part of the medical training (85.6%) and they are eager to incorporate AI in their future practice (99%). Conclusion: Currently, AI is a hot topic in medicine and dentistry. Students have a basic understanding of AI principles, a positive attitude toward AI and would like to have it incorporated into their training.

Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

Evaluation of a Novel Content-Based Image Retrieval System for the Differentiation of Interstitial Lung Diseases in CT Examinations

To evaluate the reader's diagnostic performance against the ground truth with and without the help of a novel content-based image retrieval system (CBIR) that retrieves images with similar CT patterns from a database of 79 different interstitial lung diseases. We evaluated three novice readers' and three resident physicians' (with at least three years of experience) diagnostic performance evaluating 50 different CTs featuring 10 different patterns (e.g., honeycombing, tree-in bud, ground glass, bronchiectasis, etc.) and 24 different diseases (sarcoidosis, UIP, NSIP, Aspergillosis, COVID-19 pneumonia etc.). The participants read the cases first without assistance (and without feedback regarding correctness), and with a 2-month interval in a random order with the assistance of the novel CBIR. To invoke the CBIR, a ROI is placed into the pathologic pattern by the reader and the system retrieves diseases with similar patterns. To further narrow the differential diagnosis, the readers can consult an integrated textbook and have the possibility of selecting high-level semantic features representing clinical information (chronic, infectious, smoking status, etc.). We analyzed readers' accuracy without and with CBIR assistance and further tested the hypothesis that the CBIR would help to improve diagnostic performance utilizing Wilcoxon signed rank test. The novice readers demonstrated an unassisted accuracy of 18/28/44%, and an assisted accuracy of 84/82/90%, respectively. The resident physicians demonstrated an unassisted accuracy of 56/56/70%, and an assisted accuracy of 94/90/96%, respectively. For each reader, as well as overall, Sign test demonstrated statistically significant (p < 0.01) difference between the unassisted and the assisted reads. For students and physicians, Chi²-test and Mann-Whitney-U test demonstrated statistically significant (p < 0.01) difference for unassisted reads and statistically insignificant (p > 0.01) difference for assisted reads. The evaluated CBIR relying on pattern analysis and featuring the option to filter the results of the CBIR by predominant characteristics of the diseases via selecting high-level semantic features helped to drastically improve novices' and resident physicians' accuracy in diagnosing interstitial lung diseases in CT.

Regulatory Issues and Challenges to Artificial Intelligence Adoption

Artificial intelligence technology promises to redefine the practice of radiology. However, it exists in a nascent phase and remains largely untested in the clinical space. This nature is both a cause and consequence of the uncertain legal-regulatory environment it enters. This discussion aims to shed light on these challenges, tracing the various pathways toward approval by the US Food and Drug Administration, the future of government oversight, privacy issues, ethical dilemmas, and practical considerations related to implementation in radiologist practice.

Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

Responsible Radionuclide Cancer Care

The landscape of nuclear oncology is rapidly changing. The advent of molecular radionuclide theranostics, multidisciplinary tumor board decision making, artificial intelligence and radiomics interpretation of diagnostic imaging, evolution of pharmacogenomics prediction of tumor response, and regulatory requirements for prospective individual dosimetry are just some of the elements which are broadening the essence of physician responsibility. The burgeoning knowledge base essential for mastering the emergent technologies, and their profound effect on moral philosophic aspects of provision of cancer care, are challenging. The new relationship of the theranostic nuclear physician with respect to shared care of the individual patient, particularly with regard to transparency, accountability, and responsibility for targeted radionuclide diagnosis and therapy of cancer, will be explored in this update.